diff --git a/benchmark/README.md b/benchmark/README.md deleted file mode 100644 index f0b98ab..0000000 --- a/benchmark/README.md +++ /dev/null @@ -1,518 +0,0 @@ -# Monolake Benchmark - -Monolake benchmark contains programs and scripts to benchmark monolake's performance and comparison with other popular open source proxy programs. - -## Topolonogy - -A client and a server will be setup on separated machines and traffic will go through the proxy which will be on another machine. The client machine is more powerful so that when testing it can reach or over the capabilities of the server. All machines are on a stable network environment to flat traffic varies. - -Basic test tool is wrk2 and it is installed on client machine. Nginx will be setup on server machine as web backend. Different proxy programs (monolake, nginx, traefik) will be installed and tested to compare. - -![network-topology](images/README/network-topology.png) - -We plan to benchmark monolake for 2 cases. The first case is monolake on a 4 core machine as proxy, another case is monolake on a 16 core machine. - -## Reproduce on AWS EC2 machines - -Reference aws ec2 id for client machine: standard aws linux image on c6a.8xlarge - -Reference aws ec2 id for server machine: standard aws linux image on c6a.2xlarge - -The server machine should be configured with some security group like this: - -![server-security-group](images/README/server-security-group.png) - -Reference aws ec2 id for proxy service machine: standard aws linux image on c5a.xlarge (4 cores) and c6a.4xlarge (16 cores) - -The proxy machine should be configured with some security group like this: - -![proxy-security-group](images/README/proxy-security-group.png) - -## Setup - -### Client Setup - -client/setup-once.sh will be used to install test tools on the client machine: curl, wrk2. - -```bash -cd $MONOLAKE_HOME/client -sudo yum -y install gcc git openssl-devel zlib-devel - -# download curl: it is installed by default - -# download wrk2 -cd $HOME -git clone https://github.com/giltene/wrk2 -cd wrk2 -make WITH_OPENSSL=/usr -``` - -### Server Setup - -server/setup-once.sh will be used to install nginx web server on the server machine. - -```bash -sudo yum -y install nginx -sudo mv /etc/nginx/nginx.conf /etc/nginx/nginx-original.conf -sudo cp $MONOLAKE_HOME/benchmark/server/nginx-web.conf /etc/nginx/nginx.conf -sudo openssl req -x509 -nodes -days 365 -newkey rsa:2048 -keyout /etc/nginx/cert.key -out /etc/nginx/cert.pem -sudo cat /etc/nginx/cert.key /etc/nginx/cert.pem > $MONOLAKE_HOME/combined.pem -sudo mv $MONOLAKE_HOME/combined.pem /etc/nginx/ -sudo cp -r $MONOLAKE_HOME/benchmark/server/webroot/* /usr/share/nginx/html/ -sudo service nginx restart -``` - -### Proxy Setup - -proxy/<>/setup-once.sh will be used to install proxy softwares monolake and comparisons nginx and traefik on the proxy machine. - -#### proxy/monolake/setup-once.sh - -```bash -sudo yum -y install gcc openssl-devel - -# install rust nightly -curl --proto '=https' --tlsv1.2 -sSf https://sh.rustup.rs | sh - -cd $MONOLAKE_HOME - -# generate certs -sh -c "cd examples && ./gen_cert.sh" -mkdir examples/certs && openssl req -x509 -newkey rsa:2048 -keyout examples/certs/key.pem -out examples/certs/cert.pem -sha256 -days 365 -nodes -subj "/CN=monolake.cloudwego.io" - -# build monolake -cd $MONOLAKE_HOME -cargo build --release -``` - -#### proxy/nginx/setup-once.sh - -```bash -sudo yum install -y nginx -sudo openssl req -x509 -nodes -days 365 -newkey rsa:2048 -keyout /etc/nginx/cert.key -out /etc/nginx/cert.pem -sudo cat /etc/nginx/cert.key /etc/nginx/cert.pem > $MONOLAKE_HOME/combined.pem -sudo mv $MONOLAKE_HOME/combined.pem /etc/nginx/ -``` - -#### proxy/traefik/setup-once.sh - -```bash -cd $MONOLAKE_HOME/benchmark/proxy/traefik/ -wget https://github.com/traefik/traefik/releases/download/v3.0.0-rc1/traefik_v3.0.0-rc1_linux_amd64.tar.gz -tar zxvf traefik_v3.0.0-rc1_linux_amd64.tar.gz -rm traefik_v3.0.0-rc1_linux_amd64.tar.gz -``` - -### proxy/envoy/setup-once.sh - -```bash -cd $MONOLAKE_HOME/benchmark/proxy/envoy -wget https://github.com/envoyproxy/envoy/releases/download/v1.31.0/envoy-1.31.0-linux-x86_64 -chmod +x envoy-1.31.0-linux-x86_64 -mv envoy-1.31.0-linux-x86_64 envoy -echo "Please fill all fields when generating OpenSSL certs." -sudo openssl req -x509 -nodes -days 365 -newkey rsa:2048 -keyout $MONOLAKE_HOME/benchmark/proxy/envoy/cert.key -out $MONOLAKE_HOME/benchmark/proxy/envoy/cert.pem -``` - -### Configure Server IP - -proxy/update-server-ip.sh contain scripts to update server ip in the proxy configure files. But it must be copy&pasted to console with replacing ${MONOLAKE_BENCHMARK_SERVER_IP} with real url, then run manually. Sed does not support environment variables and directly run the script will not result expected replacement. - -```bash -cd $MONOLAKE_HOME/benchmark/proxy -sed -i -e 's/127.0.0.1/${MONOLAKE_BENCHMARK_SERVER_IP}/g' nginx/nginx.conf -sed -i -e 's/127.0.0.1/${MONOLAKE_BENCHMARK_SERVER_IP}/g' monolake/monolake.toml -sed -i -e 's/127.0.0.1/${MONOLAKE_BENCHMARK_SERVER_IP}/g' traefik/traefik-dynamic.toml -``` - -### Runtime Environment Variables - -```bash -if [ -z "${MONOLAKE_HOME+set}" ]; then - export MONOLAKE_HOME=$HOME/monolake -fi - -if [ -z "${MONOLAKE_BENCHMARK_PROXY_IP+set}" ]; then - export MONOLAKE_BENCHMARK_PROXY_IP=localhost -fi - -if [ -z "${MONOLAKE_BENCHMARK_SERVER_IP+set}" ]; then - export MONOLAKE_BENCHMARK_SERVER_IP=localhost -fi -``` - -## Run Benchmark Test - -Normally we run setup-once.sh on each machine first. For proxy machine, we only need run required 1 of 3 proxy services and don't run the other 2. Also we need run update-server-ip.sh. - -Now we need make sure the setup is ready. On the client, we set environment variable MONOLAKE_BENCHMARK_SERVER_IP by: - -`export MONOLAKE_BENCHMARK_SERVER_IP=` - -then run: - -```bash -client/verify.sh -``` - -to make sure the result is expected. - -We can run benchmark test for different proxy service. For example, to benchmark monolake proxy service for http: - -```bash -client/benchmark-monolake-http.sh -``` - -Before run the benchmark, make sure MONOLAKE_BENCHMARK_SERVER_IP and MONOLAKE_BENCHMARK_PROXY_IP are set correctly. - -Check connections for down stream and up stream connections: - -```bash -netstat -tn | grep ESTAB | grep | wc -l -``` - -## Visualize the result - -### Collect the data - -#### Collect performance data - -Run performance-collect.sh on the machine which need performance data. The script can be run on client, proxy and server. For example - -```bash -./performance-collect.sh wrk # client -./performance-collect.sh monolake # proxy -./performance-collect.sh nginx # server -``` - -#### Collect latency data - -When we run benchmark using wrk2, the latency data is already generated and saved to local files. - -### Plot the data - -gnuplot is used to plot the data and the results are in .png image format. gnuplot needs to be installed. User may also copy the data to another machine with gnuplot installed, and plot the result. - -#### Plot performance data - -performance-plot.sh will be used to plot the performance data. The results are 4 .png image files: cpu-mem-usage-.png, tcp-count-.png, performance-metrices-.png, thread-count-.png. The script can be run for client, proxy and server. For example - -```bash -./performance-plot.sh wrk # client -./performance-plot.sh monolake # proxy -./performance-plot.sh nginx # server -``` - -#### Plot latency data - -There are some scripts to plot latency data in visualization/ directory. For example - -```bash -./monolake-http-latency-plot.sh -./monolake-https-latency-plot.sh -./all-http-latency-plot.sh -``` - -After running the scripts the results are in .png image format. - -## Pipeline/Automation - -We can simplify the test to pipeline/automation scripts. - -Some steps are manual steps. - -* Correct URLs/IPs: replace in all benchmark-pipeline-xxx.sh and pipeline-xxx.sh -* Avoid ssh access prompt: ssh to client/proxy service/server once -* Setup: running setup-once.sh in the directories -* Update server-ip in the configuration files: running benchmark/proxy/update-server-ip.sh and follow the sed commands - -```bash -export client_url= -export proxy_url= -export server_url= -export proxy_private_url= -export server_private_url= -``` - -Then user can use benchmark-pipeline.sh to run all test in one script. User may need type "exit" to quit some finished jobs and go to the next step. User can early input "exit" when "Writing data to CSV file wrk-performance.csv..." prompts. Finally, user will get the results and visualized images. - -Pipeline scripts contain plot scripts, so it is better to run on a host machine with GUI. Following pipeline scripts runs on OS X host and gnuplot is installed on it. If user wants to run pipeline scripts on linux/ubuntu host, please install gnuplot and use gnome-terminal as termainal tool. User may directly use non pipeline scripts on AWS EC2 linux machines. - -```bash -# new_terminal=`osascript -e 'tell app "Terminal" to do script $1'` -# new_terminal='gnome-terminal -- $1' - -export client_url= -export proxy_url= -export server_url= -export proxy_private_url= -export server_private_url= - -# manual update proxy configurations -echo "make sure proxy configurations are updated manually" -# ssh -i $HOME/ssh/monolake-benchmark.pem ec2-user@${proxy_url} -t 'cd ~/monolake/benchmark/proxy; MONOLAKE_BENCHMARK_SERVER_IP=${server_url} ./update-server-ip.sh; bash -l' - -# start server -echo "start server" -osascript -e 'tell app "Terminal" to do script "~/code/monolake/benchmark/pipeline-server.sh"' -sleep 5 - -# then start proxy nginx -echo "start proxy nginx" -osascript -e 'tell app "Terminal" to do script "~/code/monolake/benchmark/pipeline-proxy-nginx.sh"' -sleep 5 - -ssh -i $HOME/ssh/monolake-benchmark.pem ec2-user@${client_url} -t 'rm -f monolake/benchmark/wrk-performance.csv' - -# start client nginx -echo "start client nginx" -osascript -e 'tell app "Terminal" to do script "~/code/monolake/benchmark/pipeline-client-nginx.sh"' -sleep 2 - -echo "start client-metrics-collect" -ssh -i $HOME/ssh/monolake-benchmark.pem ec2-user@${client_url} -t 'cd monolake/benchmark; ~/monolake/benchmark/performance-collect.sh wrk; ~/monolake/benchmark/performance-collect.sh wrk; ~/monolake/benchmark/performance-collect.sh wrk; ~/monolake/benchmark/performance-collect.sh wrk; ~/monolake/benchmark/performance-collect.sh wrk; ~/monolake/benchmark/performance-collect.sh wrk; ~/monolake/benchmark/performance-collect.sh wrk; ~/monolake/benchmark/performance-collect.sh wrk; echo "Please type exit to continue"; bash -l' - -#stop proxy nginx -echo "stop proxy nginx" -ssh -i $HOME/ssh/monolake-benchmark.pem ec2-user@${proxy_url} -t 'cd ~/monolake/benchmark/proxy; ./stop-nginx.sh' -sleep 2 - -# then start proxy traefik -echo "start proxy traefik" -osascript -e 'tell app "Terminal" to do script "~/code/monolake/benchmark/pipeline-proxy-traefik.sh"' -sleep 5 - -# start client traefik -echo "start client" -osascript -e 'tell app "Terminal" to do script "~/code/monolake/benchmark/pipeline-client-traefik.sh"' -sleep 2 - -echo "start client-metrics-collect" -ssh -i $HOME/ssh/monolake-benchmark.pem ec2-user@${client_url} -t 'cd monolake/benchmark; ~/monolake/benchmark/performance-collect.sh wrk; ~/monolake/benchmark/performance-collect.sh wrk; ~/monolake/benchmark/performance-collect.sh wrk; ~/monolake/benchmark/performance-collect.sh wrk; ~/monolake/benchmark/performance-collect.sh wrk; ~/monolake/benchmark/performance-collect.sh wrk; ~/monolake/benchmark/performance-collect.sh wrk; ~/monolake/benchmark/performance-collect.sh wrk; echo "Please type exit to continue"; bash -l' - -#stop proxy traefik -echo "stop proxy traefik" -ssh -i $HOME/ssh/monolake-benchmark.pem ec2-user@${proxy_url} -t 'cd ~/monolake/benchmark/proxy; ./stop-traefik.sh' -sleep 2 - -# then start proxy monolake -echo "start proxy monolake" -osascript -e 'tell app "Terminal" to do script "~/code/monolake/benchmark/pipeline-proxy-monolake.sh"' -sleep 5 - -# start client -echo "start client" -osascript -e 'tell app "Terminal" to do script "~/code/monolake/benchmark/pipeline-client-monolake.sh"' -sleep 2 - -echo "start client-metrics-collect" -ssh -i $HOME/ssh/monolake-benchmark.pem ec2-user@${client_url} -t 'cd monolake/benchmark; ~/monolake/benchmark/performance-collect.sh wrk; ~/monolake/benchmark/performance-collect.sh wrk; ~/monolake/benchmark/performance-collect.sh wrk; ~/monolake/benchmark/performance-collect.sh wrk; ~/monolake/benchmark/performance-collect.sh wrk; ~/monolake/benchmark/performance-collect.sh wrk; ~/monolake/benchmark/performance-collect.sh wrk; ~/monolake/benchmark/performance-collect.sh wrk; echo "Please type exit to continue"; bash -l' - -#stop proxy monolake -echo "stop proxy monolake" -ssh -i $HOME/ssh/monolake-benchmark.pem ec2-user@${proxy_url} -t 'cd ~/monolake/benchmark/proxy; ./stop-monolake.sh' -sleep 2 - -# then start proxy envoy -echo "start proxy envoy" -osascript -e 'tell app "Terminal" to do script "~/code/monolake/benchmark/pipeline-proxy-envoy.sh"' -sleep 5 - -# start client -echo "start client" -osascript -e 'tell app "Terminal" to do script "~/code/monolake/benchmark/pipeline-client-envoy.sh"' -sleep 2 - -echo "start client-metrics-collect" -ssh -i $HOME/ssh/monolake-benchmark.pem ec2-user@${client_url} -t 'cd monolake/benchmark; ~/monolake/benchmark/performance-collect.sh wrk; ~/monolake/benchmark/performance-collect.sh wrk; ~/monolake/benchmark/performance-collect.sh wrk; ~/monolake/benchmark/performance-collect.sh wrk; ~/monolake/benchmark/performance-collect.sh wrk; ~/monolake/benchmark/performance-collect.sh wrk; ~/monolake/benchmark/performance-collect.sh wrk; ~/monolake/benchmark/performance-collect.sh wrk; echo "Please type exit to continue"; bash -l' - -#stop proxy envoy -echo "stop proxy envoy" -ssh -i $HOME/ssh/monolake-benchmark.pem ec2-user@${proxy_url} -t 'cd ~/monolake/benchmark/proxy; ./stop-envoy.sh' -sleep 2 - -#stop server -echo "stop server" -ssh -i $HOME/ssh/monolake-benchmark.pem ec2-user@${server_url} -t 'sudo service nginx stop' -sleep 2 - -echo "visualize" -cd visualization/ - -# copy collected data from client -echo "copy collected data from client" -scp -i $HOME/ssh/monolake-benchmark.pem ec2-user@${client_url}:wrk-performance.csv . -scp -i $HOME/ssh/monolake-benchmark.pem ec2-user@${client_url}:"wrk2/*.txt" . - -#copy collected data from server -echo "copy collected data from server" -scp -i $HOME/ssh/monolake-benchmark.pem ec2-user@${server_url}:nginx-performance.csv ./server-performance.csv - -#copy collected data from proxy -echo "copy collected data from proxy" -scp -i $HOME/ssh/monolake-benchmark.pem ec2-user@${proxy_url}:nginx-performance.csv . -scp -i $HOME/ssh/monolake-benchmark.pem ec2-user@${proxy_url}:traefik-performance.csv . -scp -i $HOME/ssh/monolake-benchmark.pem ec2-user@${proxy_url}:monolake-performance.csv . -scp -i $HOME/ssh/monolake-benchmark.pem ec2-user@${proxy_url}:envoy-performance.csv . - -#plot data -echo "plot data" -./performance-plot.sh nginx -./performance-plot.sh traefik -./performance-plot.sh monolake -./performance-plot.sh envoy -./performance-plot.sh server -./performance-plot.sh wrk -./nginx-http-latency-plot.sh -./traefik-http-latency-plot.sh -./monolake-http-latency-plot.sh -./envoy-http-latency-plot.sh -./all-http-latency-plot.sh -./nginx-https-latency-plot.sh -./traefik-https-latency-plot.sh -./monolake-https-latency-plot.sh -./envoy-https-latency-plot.sh -./all-https-latency-plot.sh -./proxies-performance-plot.sh -``` - -The visualized result example: - -Proxy service/monolake system performance: - -![performance-metrices-monolake](images/README/performance-metrices-monolake.png) - -Client/wrk2 http proxy services latency: - -Client/wrk2 tiny payload http proxy services latency: - -![all-http-tiny-latency](images/README/all-http-tiny-latency.png) - -Client/wrk2 small payload http proxy services latency: - -![all-http-small-latency](images/README/all-http-small-latency.png) - -Client/wrk2 medium payload http proxy services latency: - -![all-http-medium-latency](images/README/all-http-medium-latency.png) - -Client/wrk2 large payload http proxy services latency: - -![all-http-large-latency](images/README/all-http-large-latency.png) - -Client/wrk2 overall http proxy services latency: - -![all-http-latency](images/README/all-http-latency.png) - -Client/wrk2 https proxy services latency: - -Client/wrk2 tiny payload https proxy services latency: - -![all-latency-https-tiny](images/README/all-latency-https-tiny.png) - -Client/wrk2 small payload https proxy services latency: - -![all-latency-https-small](images/README/all-latency-https-small.png) - -Client/wrk2 medium payload https proxy services latency: - -![all-latency-https-medium](images/README/all-latency-https-medium.png) - -Client/wrk2 large payload https proxy services latency: - -![all-latency-https-large](images/README/all-latency-https-large.png) - -Client/wrk2 overall https proxy services latency: - -![all-latency-https](images/README/all-latency-https.png) - -Throughput and requests per second compare: - - -| Case | Requests/sec | Transfer/sec | Server Error | Timeout | -| ------------------------------- | ------------ | ------------- | ------------ | ------- | -| http-result-4c-monolake-tiny | 161101.29 | 54934896.64 | 0 | 0 | -| http-result-4c-monolake-small | 151977.67 | 529488936.96 | 0 | 0 | -| http-result-4c-monolake-medium | 85893.56 | 987842478.08 | 0 | 0 | -| http-result-4c-monolake-large | 9186.06 | 1524713390.08 | 0 | 618 | -| http-result-4c-nginx-tiny | 187973.44 | 68608327.68 | 0 | 0 | -| http-result-4c-nginx-small | 176318.84 | 618523525.12 | 0 | 0 | -| http-result-4c-nginx-medium | 108853.56 | 1256277934.08 | 0 | 0 | -| http-result-4c-nginx-large | 9304.14 | 1546188226.56 | 0 | 22 | -| http-result-4c-traefik-tiny | 9991.09 | 3407872.00 | 0 | 0 | -| http-result-4c-traefik-small | 10989.34 | 38283509.76 | 0 | 0 | -| http-result-4c-traefik-medium | 11988.52 | 137688514.56 | 0 | 0 | -| http-result-4c-traefik-large | 8737.57 | 1449551462.40 | 0 | 0 | -| http-result-4c-envoy-tiny | 36951.75 | 13600030.72 | 0 | 0 | -| http-result-4c-envoy-small | 35367.03 | 124182855.68 | 0 | 0 | -| http-result-4c-envoy-medium | 29637.89 | 341206630.40 | 0 | 0 | -| http-result-4c-envoy-large | 9285.01 | 1546188226.56 | 0 | 46 | -| https-result-4c-monolake-tiny | 141883.70 | 48381296.64 | 0 | 0 | -| https-result-4c-monolake-small | 116831.85 | 407046717.44 | 0 | 0 | -| https-result-4c-monolake-medium | 63390.91 | 728047288.32 | 0 | 0 | -| https-result-4c-monolake-large | 7946.21 | 1320702443.52 | 0 | 0 | -| https-result-4c-nginx-tiny | 127167.08 | 46420459.52 | 0 | 0 | -| https-result-4c-nginx-small | 114350.27 | 401143234.56 | 0 | 0 | -| https-result-4c-nginx-medium | 62450.58 | 718746419.20 | 0 | 0 | -| https-result-4c-nginx-large | 7881.00 | 1309965025.28 | 0 | 15 | -| https-result-4c-traefik-tiny | 9943.28 | 3386900.48 | 0 | 0 | -| https-result-4c-traefik-small | 11888.59 | 41418752.00 | 0 | 0 | -| https-result-4c-traefik-medium | 13914.15 | 159802982.40 | 0 | 0 | -| https-result-4c-traefik-large | 7698.61 | 1277752770.56 | 0 | 0 | -| https-result-4c-envoy-tiny | 34158.00 | 12582912.00 | 0 | 0 | -| https-result-4c-envoy-small | 33054.16 | 116066877.44 | 0 | 0 | -| https-result-4c-envoy-medium | 26968.24 | 310472867.84 | 0 | 0 | -| https-result-4c-envoy-large | 8349.09 | 1385126952.96 | 0 | 0 | - -![proxies-performance](images/README/proxies-performance.png) - -Throughput and requests per second compare by payload size: - - -| Case | Tiny Requests/sec | Small Requests/sec | Medium Requests/sec | Large Requests/sec | Tiny Transfer/sec | Small Transfer/sec | Medium Transfer/sec | Large Transfer/sec | -| ------------------------ | ----------------- | ------------------ | ------------------- | ------------------ | ----------------- | ------------------ | ------------------- | ------------------ | -| http-result-4c-monolake | 161101.29 | 151977.67 | 85893.56 | 9186.06 | 54934896.64 | 529488936.96 | 987842478.08 | 1524713390.08 | -| http-result-4c-nginx | 187973.44 | 176318.84 | 108853.56 | 9304.14 | 68608327.68 | 618523525.12 | 1256277934.08 | 1546188226.56 | -| http-result-4c-traefik | 9991.09 | 10989.34 | 11988.52 | 8737.57 | 3407872.00 | 38283509.76 | 137688514.56 | 1449551462.40 | -| http-result-4c-envoy | 36951.75 | 35367.03 | 29637.89 | 9285.01 | 13600030.72 | 124182855.68 | 341206630.40 | 1546188226.56 | -| https-result-4c-monolake | 141883.70 | 116831.85 | 63390.91 | 7946.21 | 48381296.64 | 407046717.44 | 728047288.32 | 1320702443.52 | -| https-result-4c-nginx | 127167.08 | 114350.27 | 62450.58 | 7881.00 | 46420459.52 | 401143234.56 | 718746419.20 | 1309965025.28 | -| https-result-4c-traefik | 9943.28 | 11888.59 | 13914.15 | 7698.61 | 3386900.48 | 41418752.00 | 159802982.40 | 1277752770.56 | -| https-result-4c-envoy | 34158.00 | 33054.16 | 26968.24 | 8349.09 | 12582912.00 | 116066877.44 | 310472867.84 | 1385126952.96 | -<<<<<<< HEAD - -Tiny payload QPS(requests per second): - -![QPS of tiny payload](images/README/tiny-qps.png) - -Small payload QPS(requests per second): - -![QPS of small payload](images/README/small-qps.png) - -Medium payload QPS(requests per second): - -![QPS of medium payload](images/README/medium-qps.png) - -Large payload QPS(requests per second): - -![QPS of large payload](images/README/large-qps.png) - -Tiny payload throughput: - -![Throughput of tiny payload](images/README/tiny-throughput.png) - -Small payload throughput: - -![Throughput of small payload](images/README/small-throughput.png) - -Medium payload throughput: - -![Throughput of medium payload](images/README/medium-throughput.png) - -Large payload throughput: - -![Throughput of large payload](images/README/large-throughput.png) - -Overall throughput and requests per second: - -![proxies-performance-rotated](images/README/proxies-performance-rotated.png) diff --git a/benchmark/benchmark-pipeline.sh b/benchmark/benchmark-pipeline.sh deleted file mode 100755 index 510c137..0000000 --- a/benchmark/benchmark-pipeline.sh +++ /dev/null @@ -1,135 +0,0 @@ -# new_terminal=`osascript -e 'tell app "Terminal" to do script $1'` -# new_terminal='gnome-terminal -- $1' - -export client_url=3.133.229.116 -export proxy_url=18.217.152.113 -export server_url=3.22.140.218 -export proxy_private_url=172.31.2.253 -export server_private_url=172.31.22.170 - -# manual update proxy configurations -echo "make sure proxy configurations are updated manually" -# ssh -i $HOME/ssh/monolake-benchmark.pem ec2-user@${proxy_url} -t 'cd ~/monolake/benchmark/proxy; MONOLAKE_BENCHMARK_SERVER_IP=${server_url} ./update-server-ip.sh; bash -l' - -# start server -echo "start server" -osascript -e 'tell app "Terminal" to do script "~/code/monolake/benchmark/pipeline-server.sh"' -sleep 5 - -# then start proxy nginx -echo "start proxy nginx" -osascript -e 'tell app "Terminal" to do script "~/code/monolake/benchmark/pipeline-proxy-nginx.sh"' -sleep 5 - -ssh -i $HOME/ssh/monolake-benchmark.pem ec2-user@${client_url} -t 'rm -f monolake/benchmark/wrk-performance.csv' - -# start client nginx -echo "start client nginx" -osascript -e 'tell app "Terminal" to do script "~/code/monolake/benchmark/pipeline-client-nginx.sh"' -sleep 2 - -echo "start client-metrics-collect" -ssh -i $HOME/ssh/monolake-benchmark.pem ec2-user@${client_url} -t 'cd monolake/benchmark; ~/monolake/benchmark/performance-collect.sh wrk; ~/monolake/benchmark/performance-collect.sh wrk; ~/monolake/benchmark/performance-collect.sh wrk; ~/monolake/benchmark/performance-collect.sh wrk; ~/monolake/benchmark/performance-collect.sh wrk; ~/monolake/benchmark/performance-collect.sh wrk; ~/monolake/benchmark/performance-collect.sh wrk; ~/monolake/benchmark/performance-collect.sh wrk; echo "Please type exit to continue"; bash -l' - -#stop proxy nginx -echo "stop proxy nginx" -ssh -i $HOME/ssh/monolake-benchmark.pem ec2-user@${proxy_url} -t 'cd ~/monolake/benchmark/proxy; ./stop-nginx.sh' -sleep 2 - -# then start proxy traefik -echo "start proxy traefik" -osascript -e 'tell app "Terminal" to do script "~/code/monolake/benchmark/pipeline-proxy-traefik.sh"' -sleep 5 - -# start client traefik -echo "start client" -osascript -e 'tell app "Terminal" to do script "~/code/monolake/benchmark/pipeline-client-traefik.sh"' -sleep 2 - -echo "start client-metrics-collect" -ssh -i $HOME/ssh/monolake-benchmark.pem ec2-user@${client_url} -t 'cd monolake/benchmark; ~/monolake/benchmark/performance-collect.sh wrk; ~/monolake/benchmark/performance-collect.sh wrk; ~/monolake/benchmark/performance-collect.sh wrk; ~/monolake/benchmark/performance-collect.sh wrk; ~/monolake/benchmark/performance-collect.sh wrk; ~/monolake/benchmark/performance-collect.sh wrk; ~/monolake/benchmark/performance-collect.sh wrk; ~/monolake/benchmark/performance-collect.sh wrk; echo "Please type exit to continue"; bash -l' - -#stop proxy traefik -echo "stop proxy traefik" -ssh -i $HOME/ssh/monolake-benchmark.pem ec2-user@${proxy_url} -t 'cd ~/monolake/benchmark/proxy; ./stop-traefik.sh' -sleep 2 - -# then start proxy monolake -echo "start proxy monolake" -osascript -e 'tell app "Terminal" to do script "~/code/monolake/benchmark/pipeline-proxy-monolake.sh"' -sleep 5 - -# start client -echo "start client" -osascript -e 'tell app "Terminal" to do script "~/code/monolake/benchmark/pipeline-client-monolake.sh"' -sleep 2 - -echo "start client-metrics-collect" -ssh -i $HOME/ssh/monolake-benchmark.pem ec2-user@${client_url} -t 'cd monolake/benchmark; ~/monolake/benchmark/performance-collect.sh wrk; ~/monolake/benchmark/performance-collect.sh wrk; ~/monolake/benchmark/performance-collect.sh wrk; ~/monolake/benchmark/performance-collect.sh wrk; ~/monolake/benchmark/performance-collect.sh wrk; ~/monolake/benchmark/performance-collect.sh wrk; ~/monolake/benchmark/performance-collect.sh wrk; ~/monolake/benchmark/performance-collect.sh wrk; echo "Please type exit to continue"; bash -l' - -#stop proxy monolake -echo "stop proxy monolake" -ssh -i $HOME/ssh/monolake-benchmark.pem ec2-user@${proxy_url} -t 'cd ~/monolake/benchmark/proxy; ./stop-monolake.sh' -sleep 2 - -# then start proxy envoy -echo "start proxy envoy" -osascript -e 'tell app "Terminal" to do script "~/code/monolake/benchmark/pipeline-proxy-envoy.sh"' -sleep 5 - -# start client -echo "start client" -osascript -e 'tell app "Terminal" to do script "~/code/monolake/benchmark/pipeline-client-envoy.sh"' -sleep 2 - -echo "start client-metrics-collect" -ssh -i $HOME/ssh/monolake-benchmark.pem ec2-user@${client_url} -t 'cd monolake/benchmark; ~/monolake/benchmark/performance-collect.sh wrk; ~/monolake/benchmark/performance-collect.sh wrk; ~/monolake/benchmark/performance-collect.sh wrk; ~/monolake/benchmark/performance-collect.sh wrk; ~/monolake/benchmark/performance-collect.sh wrk; ~/monolake/benchmark/performance-collect.sh wrk; ~/monolake/benchmark/performance-collect.sh wrk; ~/monolake/benchmark/performance-collect.sh wrk; echo "Please type exit to continue"; bash -l' - -#stop proxy envoy -echo "stop proxy envoy" -ssh -i $HOME/ssh/monolake-benchmark.pem ec2-user@${proxy_url} -t 'cd ~/monolake/benchmark/proxy; ./stop-envoy.sh' -sleep 2 - -#stop server -echo "stop server" -ssh -i $HOME/ssh/monolake-benchmark.pem ec2-user@${server_url} -t 'sudo service nginx stop' -sleep 2 - -echo "visualize" -cd visualization/ - -# copy collected data from client -echo "copy collected data from client" -scp -i $HOME/ssh/monolake-benchmark.pem ec2-user@${client_url}:wrk-performance.csv . -scp -i $HOME/ssh/monolake-benchmark.pem ec2-user@${client_url}:"wrk2/*.txt" . - -#copy collected data from server -echo "copy collected data from server" -scp -i $HOME/ssh/monolake-benchmark.pem ec2-user@${server_url}:nginx-performance.csv ./server-performance.csv - -#copy collected data from proxy -echo "copy collected data from proxy" -scp -i $HOME/ssh/monolake-benchmark.pem ec2-user@${proxy_url}:nginx-performance.csv . -scp -i $HOME/ssh/monolake-benchmark.pem ec2-user@${proxy_url}:traefik-performance.csv . -scp -i $HOME/ssh/monolake-benchmark.pem ec2-user@${proxy_url}:monolake-performance.csv . -scp -i $HOME/ssh/monolake-benchmark.pem ec2-user@${proxy_url}:envoy-performance.csv . - -#plot data -echo "plot data" -./performance-plot.sh nginx -./performance-plot.sh traefik -./performance-plot.sh monolake -./performance-plot.sh envoy -./performance-plot.sh server -./performance-plot.sh wrk -./nginx-http-latency-plot.sh -./traefik-http-latency-plot.sh -./monolake-http-latency-plot.sh -./envoy-http-latency-plot.sh -./all-http-latency-plot.sh -./nginx-https-latency-plot.sh -./traefik-https-latency-plot.sh -./monolake-https-latency-plot.sh -./envoy-https-latency-plot.sh -./all-https-latency-plot.sh -./proxies-performance-plot.sh diff --git a/benchmark/client/benchmark-envoy-http.sh b/benchmark/client/benchmark-envoy-http.sh deleted file mode 100755 index 5eb6558..0000000 --- a/benchmark/client/benchmark-envoy-http.sh +++ /dev/null @@ -1,20 +0,0 @@ -# run benchmark: make sure proxy and server all are running; run this script from client -if [ -z "${MONOLAKE_HOME+set}" ]; then - export MONOLAKE_HOME=$HOME/monolake -fi - -if [ -z "${MONOLAKE_BENCHMARK_PROXY_IP+set}" ]; then - export MONOLAKE_BENCHMARK_PROXY_IP=localhost -fi - -if [ -z "${MONOLAKE_BENCHMARK_SERVER_IP+set}" ]; then - export MONOLAKE_BENCHMARK_SERVER_IP=localhost -fi - -cd $HOME/wrk2 - -# http proxy for envoy -./wrk -d 1m -c 640 -t 64 -R 210000 --latency http://$MONOLAKE_BENCHMARK_PROXY_IP:8500/server2/ > http-result-4c-envoy-tiny.txt -./wrk -d 1m -c 640 -t 64 -R 200000 --latency http://$MONOLAKE_BENCHMARK_PROXY_IP:8500/server3/ > http-result-4c-envoy-small.txt -./wrk -d 1m -c 640 -t 64 -R 120000 --latency http://$MONOLAKE_BENCHMARK_PROXY_IP:8500/server4/ > http-result-4c-envoy-medium.txt -./wrk -d 1m -c 640 -t 64 -R 10000 --latency http://$MONOLAKE_BENCHMARK_PROXY_IP:8500/server5/ > http-result-4c-envoy-large.txt diff --git a/benchmark/client/benchmark-envoy-https.sh b/benchmark/client/benchmark-envoy-https.sh deleted file mode 100755 index 4900e92..0000000 --- a/benchmark/client/benchmark-envoy-https.sh +++ /dev/null @@ -1,20 +0,0 @@ -# run benchmark: make sure proxy and server all are running; run this script from client -if [ -z "${MONOLAKE_HOME+set}" ]; then - export MONOLAKE_HOME=$HOME/monolake -fi - -if [ -z "${MONOLAKE_BENCHMARK_PROXY_IP+set}" ]; then - export MONOLAKE_BENCHMARK_PROXY_IP=localhost -fi - -if [ -z "${MONOLAKE_BENCHMARK_SERVER_IP+set}" ]; then - export MONOLAKE_BENCHMARK_SERVER_IP=localhost -fi - -cd $HOME/wrk2 - -# https proxy for envoy -./wrk -d 1m -c 640 -t 64 -R 150000 --latency https://$MONOLAKE_BENCHMARK_PROXY_IP:5443/server2/ > https-result-4c-envoy-tiny.txt -./wrk -d 1m -c 640 -t 64 -R 140000 --latency https://$MONOLAKE_BENCHMARK_PROXY_IP:5443/server3/ > https-result-4c-envoy-small.txt -./wrk -d 1m -c 640 -t 64 -R 80000 --latency https://$MONOLAKE_BENCHMARK_PROXY_IP:5443/server4/ > https-result-4c-envoy-medium.txt -./wrk -d 1m -c 640 -t 64 -R 10000 --latency https://$MONOLAKE_BENCHMARK_PROXY_IP:5443/server5/ > https-result-4c-envoy-large.txt diff --git a/benchmark/client/benchmark-monolake-16core-http.sh b/benchmark/client/benchmark-monolake-16core-http.sh deleted file mode 100755 index 0428f74..0000000 --- a/benchmark/client/benchmark-monolake-16core-http.sh +++ /dev/null @@ -1,26 +0,0 @@ -# run benchmark: make sure proxy and server all are running; run this script from client -if [ -z "${MONOLAKE_HOME+set}" ]; then - export MONOLAKE_HOME=$HOME/monolake -fi - -if [ -z "${MONOLAKE_BENCHMARK_PROXY_IP+set}" ]; then - export MONOLAKE_BENCHMARK_PROXY_IP=localhost -fi - -if [ -z "${MONOLAKE_BENCHMARK_SERVER_IP+set}" ]; then - export MONOLAKE_BENCHMARK_SERVER_IP=localhost -fi - -cd $HOME/wrk2 - -# http proxy for monolake -./wrk -d 1m -c 1000 -t 20 -R 2000 --latency http://$MONOLAKE_BENCHMARK_PROXY_IP:8402 > http-result-16c-monolake-tiny.txt -./wrk -d 1m -c 1000 -t 20 -R 2000 --latency http://$MONOLAKE_BENCHMARK_PROXY_IP:8403 > http-result-16c-monolake-small.txt -./wrk -d 1m -c 1000 -t 20 -R 2000 --latency http://$MONOLAKE_BENCHMARK_PROXY_IP:8404 > http-result-16c-monolake-medium.txt -./wrk -d 1m -c 1000 -t 20 -R 2000 --latency http://$MONOLAKE_BENCHMARK_PROXY_IP:8405 > http-result-16c-monolake-large.txt - -# http proxy for haproxy (not used) -# ./wrk -d 1m -c 1000 -t 20 -R 2000 --latency http://$MONOLAKE_BENCHMARK_PROXY_IP:8200/server2 > http-result-16c-haproxy-tiny.txt -# ./wrk -d 1m -c 1000 -t 20 -R 2000 --latency http://$MONOLAKE_BENCHMARK_PROXY_IP:8200/server3 > http-result-16c-haproxy-small.txt -# ./wrk -d 1m -c 1000 -t 20 -R 2000 --latency http://$MONOLAKE_BENCHMARK_PROXY_IP:8200/server4 > http-result-16c-haproxy-medium.txt -# ./wrk -d 1m -c 1000 -t 20 -R 2000 --latency http://$MONOLAKE_BENCHMARK_PROXY_IP:8200/server5 > http-result-16c-haproxy-large.txt diff --git a/benchmark/client/benchmark-monolake-16core-https.sh b/benchmark/client/benchmark-monolake-16core-https.sh deleted file mode 100755 index bb81fe9..0000000 --- a/benchmark/client/benchmark-monolake-16core-https.sh +++ /dev/null @@ -1,26 +0,0 @@ -# run benchmark: make sure proxy and server all are running; run this script from client -if [ -z "${MONOLAKE_HOME+set}" ]; then - export MONOLAKE_HOME=$HOME/monolake -fi - -if [ -z "${MONOLAKE_BENCHMARK_PROXY_IP+set}" ]; then - export MONOLAKE_BENCHMARK_PROXY_IP=localhost -fi - -if [ -z "${MONOLAKE_BENCHMARK_SERVER_IP+set}" ]; then - export MONOLAKE_BENCHMARK_SERVER_IP=localhost -fi - -cd $HOME/wrk2 - -# https proxy for monolake -./wrk -d 1m -c 1000 -t 20 -R 2000 --latency https://$MONOLAKE_BENCHMARK_PROXY_IP:6442 > https-result-16c-monolake-tiny.txt -./wrk -d 1m -c 1000 -t 20 -R 2000 --latency https://$MONOLAKE_BENCHMARK_PROXY_IP:6443 > https-result-16c-monolake-small.txt -./wrk -d 1m -c 1000 -t 20 -R 2000 --latency https://$MONOLAKE_BENCHMARK_PROXY_IP:6444 > https-result-16c-monolake-medium.txt -./wrk -d 1m -c 1000 -t 20 -R 2000 --latency https://$MONOLAKE_BENCHMARK_PROXY_IP:6445 > https-result-16c-monolake-large.txt - -# https proxy for haproxy (not used) -# ./wrk -d 1m -c 1000 -t 20 -R 2000 --latency https://$MONOLAKE_BENCHMARK_PROXY_IP:9443/server2 > https-result-16c-haproxy-tiny.txt -# ./wrk -d 1m -c 1000 -t 20 -R 2000 --latency https://$MONOLAKE_BENCHMARK_PROXY_IP:9443/server3 > https-result-16c-haproxy-small.txt -# ./wrk -d 1m -c 1000 -t 20 -R 2000 --latency https://$MONOLAKE_BENCHMARK_PROXY_IP:9443/server4 > https-result-16c-haproxy-medium.txt -# ./wrk -d 1m -c 1000 -t 20 -R 2000 --latency https://$MONOLAKE_BENCHMARK_PROXY_IP:9443/server5 > https-result-16c-haproxy-large.txt diff --git a/benchmark/client/benchmark-monolake-http.sh b/benchmark/client/benchmark-monolake-http.sh deleted file mode 100755 index f06c239..0000000 --- a/benchmark/client/benchmark-monolake-http.sh +++ /dev/null @@ -1,36 +0,0 @@ -# run benchmark: make sure proxy and server all are running; run this script from client -if [ -z "${MONOLAKE_HOME+set}" ]; then - export MONOLAKE_HOME=$HOME/monolake -fi - -if [ -z "${MONOLAKE_BENCHMARK_PROXY_IP+set}" ]; then - export MONOLAKE_BENCHMARK_PROXY_IP=localhost -fi - -if [ -z "${MONOLAKE_BENCHMARK_SERVER_IP+set}" ]; then - export MONOLAKE_BENCHMARK_SERVER_IP=localhost -fi - -cd $HOME/wrk2 - -# http proxy for monolake -./wrk -d 1m -c 640 -t 64 -R 200000 --latency http://$MONOLAKE_BENCHMARK_PROXY_IP:8402 > http-result-4c-monolake-tiny.txt -./wrk -d 1m -c 640 -t 64 -R 180000 --latency http://$MONOLAKE_BENCHMARK_PROXY_IP:8403 > http-result-4c-monolake-small.txt -./wrk -d 1m -c 640 -t 64 -R 100000 --latency http://$MONOLAKE_BENCHMARK_PROXY_IP:8404 > http-result-4c-monolake-medium.txt -./wrk -d 1m -c 640 -t 64 -R 100000 --latency http://$MONOLAKE_BENCHMARK_PROXY_IP:8405 > http-result-4c-monolake-large.txt - -# ./wrk -d 1m -c 3500 -t 20 -R 80000 --latency http://$MONOLAKE_BENCHMARK_PROXY_IP:8402 > http-result-4c-monolake-tiny.txt -# ./wrk -d 1m -c 3500 -t 20 -R 73000 --latency http://$MONOLAKE_BENCHMARK_PROXY_IP:8403 > http-result-4c-monolake-small.txt -# ./wrk -d 1m -c 3500 -t 20 -R 70000 --latency http://$MONOLAKE_BENCHMARK_PROXY_IP:8404 > http-result-4c-monolake-medium.txt -# ./wrk -d 1m -c 120 -t 20 -R 7500 --latency http://$MONOLAKE_BENCHMARK_PROXY_IP:8405 > http-result-4c-monolake-large.txt - -# ./wrk -d 1m -c 1447 -t 20 -R 16000 --latency http://$MONOLAKE_BENCHMARK_PROXY_IP:8402 > http-result-4c-monolake-tiny.txt -# ./wrk -d 1m -c 1447 -t 20 -R 20000 --latency http://$MONOLAKE_BENCHMARK_PROXY_IP:8403 > http-result-4c-monolake-small.txt -# ./wrk -d 1m -c 1447 -t 20 -R 16000 --latency http://$MONOLAKE_BENCHMARK_PROXY_IP:8404 > http-result-4c-monolake-medium.txt -# ./wrk -d 1m -c 1200 -t 20 -R 4000 --latency http://$MONOLAKE_BENCHMARK_PROXY_IP:8405 > http-result-4c-monolake-large.txt - -# http proxy for haproxy (not used) -# ./wrk -d 1m -c 1000 -t 20 -R 2000 --latency http://$MONOLAKE_BENCHMARK_PROXY_IP:8200/server2 > http-result-4c-haproxy-tiny.txt -# ./wrk -d 1m -c 1000 -t 20 -R 2000 --latency http://$MONOLAKE_BENCHMARK_PROXY_IP:8200/server3 > http-result-4c-haproxy-small.txt -# ./wrk -d 1m -c 1000 -t 20 -R 2000 --latency http://$MONOLAKE_BENCHMARK_PROXY_IP:8200/server4 > http-result-4c-haproxy-medium.txt -# ./wrk -d 1m -c 1000 -t 20 -R 2000 --latency http://$MONOLAKE_BENCHMARK_PROXY_IP:8200/server5 > http-result-4c-haproxy-large.txt diff --git a/benchmark/client/benchmark-monolake-https.sh b/benchmark/client/benchmark-monolake-https.sh deleted file mode 100755 index 65582c3..0000000 --- a/benchmark/client/benchmark-monolake-https.sh +++ /dev/null @@ -1,36 +0,0 @@ -# run benchmark: make sure proxy and server all are running; run this script from client -if [ -z "${MONOLAKE_HOME+set}" ]; then - export MONOLAKE_HOME=$HOME/monolake -fi - -if [ -z "${MONOLAKE_BENCHMARK_PROXY_IP+set}" ]; then - export MONOLAKE_BENCHMARK_PROXY_IP=localhost -fi - -if [ -z "${MONOLAKE_BENCHMARK_SERVER_IP+set}" ]; then - export MONOLAKE_BENCHMARK_SERVER_IP=localhost -fi - -cd $HOME/wrk2 - -# https proxy for monolake -./wrk -d 1m -c 640 -t 64 -R 160000 --latency https://$MONOLAKE_BENCHMARK_PROXY_IP:6442 > https-result-4c-monolake-tiny.txt -./wrk -d 1m -c 640 -t 64 -R 140000 --latency https://$MONOLAKE_BENCHMARK_PROXY_IP:6443 > https-result-4c-monolake-small.txt -./wrk -d 1m -c 640 -t 64 -R 80000 --latency https://$MONOLAKE_BENCHMARK_PROXY_IP:6444 > https-result-4c-monolake-medium.txt -./wrk -d 1m -c 640 -t 64 -R 9000 --latency https://$MONOLAKE_BENCHMARK_PROXY_IP:6445 > https-result-4c-monolake-large.txt - -# ./wrk -d 1m -c 1447 -t 20 -R 70000 --latency https://$MONOLAKE_BENCHMARK_PROXY_IP:6442 > https-result-4c-monolake-tiny.txt -# ./wrk -d 1m -c 1447 -t 20 -R 62000 --latency https://$MONOLAKE_BENCHMARK_PROXY_IP:6443 > https-result-4c-monolake-small.txt -# ./wrk -d 1m -c 1447 -t 20 -R 60000 --latency https://$MONOLAKE_BENCHMARK_PROXY_IP:6444 > https-result-4c-monolake-medium.txt -# ./wrk -d 1m -c 1447 -t 20 -R 6000 --latency https://$MONOLAKE_BENCHMARK_PROXY_IP:6445 > https-result-4c-monolake-large.txt - -# ./wrk -d 1m -c 1000 -t 20 -R 2000 --latency https://$MONOLAKE_BENCHMARK_PROXY_IP:6442 > https-result-4c-monolake-tiny.txt -# ./wrk -d 1m -c 1000 -t 20 -R 2000 --latency https://$MONOLAKE_BENCHMARK_PROXY_IP:6443 > https-result-4c-monolake-small.txt -# ./wrk -d 1m -c 1000 -t 20 -R 2000 --latency https://$MONOLAKE_BENCHMARK_PROXY_IP:6444 > https-result-4c-monolake-medium.txt -# ./wrk -d 1m -c 1000 -t 20 -R 2000 --latency https://$MONOLAKE_BENCHMARK_PROXY_IP:6445 > https-result-4c-monolake-large.txt - -# https proxy for haproxy (not used) -# ./wrk -d 1m -c 1000 -t 20 -R 2000 --latency https://$MONOLAKE_BENCHMARK_PROXY_IP:9443/server2 > https-result-4c-haproxy-tiny.txt -# ./wrk -d 1m -c 1000 -t 20 -R 2000 --latency https://$MONOLAKE_BENCHMARK_PROXY_IP:9443/server3 > https-result-4c-haproxy-small.txt -# ./wrk -d 1m -c 1000 -t 20 -R 2000 --latency https://$MONOLAKE_BENCHMARK_PROXY_IP:9443/server4 > https-result-4c-haproxy-medium.txt -# ./wrk -d 1m -c 1000 -t 20 -R 2000 --latency https://$MONOLAKE_BENCHMARK_PROXY_IP:9443/server5 > https-result-4c-haproxy-large.txt diff --git a/benchmark/client/benchmark-nginx-16core-http.sh b/benchmark/client/benchmark-nginx-16core-http.sh deleted file mode 100755 index 75e1c8c..0000000 --- a/benchmark/client/benchmark-nginx-16core-http.sh +++ /dev/null @@ -1,20 +0,0 @@ -# run benchmark: make sure proxy and server all are running; run this script from client -if [ -z "${MONOLAKE_HOME+set}" ]; then - export MONOLAKE_HOME=$HOME/monolake -fi - -if [ -z "${MONOLAKE_BENCHMARK_PROXY_IP+set}" ]; then - export MONOLAKE_BENCHMARK_PROXY_IP=localhost -fi - -if [ -z "${MONOLAKE_BENCHMARK_SERVER_IP+set}" ]; then - export MONOLAKE_BENCHMARK_SERVER_IP=localhost -fi - -cd $HOME/wrk2 - -# http proxy for nginx -./wrk -d 1m -c 1000 -t 20 -R 2000 --latency http://$MONOLAKE_BENCHMARK_PROXY_IP:8100/server2 > http-result-16c-nginx-tiny.txt -./wrk -d 1m -c 1000 -t 20 -R 2000 --latency http://$MONOLAKE_BENCHMARK_PROXY_IP:8100/server3 > http-result-16c-nginx-small.txt -./wrk -d 1m -c 1000 -t 20 -R 2000 --latency http://$MONOLAKE_BENCHMARK_PROXY_IP:8100/server4 > http-result-16c-nginx-medium.txt -./wrk -d 1m -c 1000 -t 20 -R 2000 --latency http://$MONOLAKE_BENCHMARK_PROXY_IP:8100/server5 > http-result-16c-nginx-large.txt diff --git a/benchmark/client/benchmark-nginx-16core-https.sh b/benchmark/client/benchmark-nginx-16core-https.sh deleted file mode 100755 index e345288..0000000 --- a/benchmark/client/benchmark-nginx-16core-https.sh +++ /dev/null @@ -1,20 +0,0 @@ -# run benchmark: make sure proxy and server all are running; run this script from client -if [ -z "${MONOLAKE_HOME+set}" ]; then - export MONOLAKE_HOME=$HOME/monolake -fi - -if [ -z "${MONOLAKE_BENCHMARK_PROXY_IP+set}" ]; then - export MONOLAKE_BENCHMARK_PROXY_IP=localhost -fi - -if [ -z "${MONOLAKE_BENCHMARK_SERVER_IP+set}" ]; then - export MONOLAKE_BENCHMARK_SERVER_IP=localhost -fi - -cd $HOME/wrk2 - -# https proxy for nginx -./wrk -d 1m -c 1000 -t 20 -R 2000 --latency https://$MONOLAKE_BENCHMARK_PROXY_IP:8443/server2 > https-result-16c-nginx-tiny.txt -./wrk -d 1m -c 1000 -t 20 -R 2000 --latency https://$MONOLAKE_BENCHMARK_PROXY_IP:8443/server3 > https-result-16c-nginx-small.txt -./wrk -d 1m -c 1000 -t 20 -R 2000 --latency https://$MONOLAKE_BENCHMARK_PROXY_IP:8443/server4 > https-result-16c-nginx-medium.txt -./wrk -d 1m -c 1000 -t 20 -R 2000 --latency https://$MONOLAKE_BENCHMARK_PROXY_IP:8443/server5 > https-result-16c-nginx-large.txt diff --git a/benchmark/client/benchmark-nginx-http.sh b/benchmark/client/benchmark-nginx-http.sh deleted file mode 100755 index 931fd9f..0000000 --- a/benchmark/client/benchmark-nginx-http.sh +++ /dev/null @@ -1,30 +0,0 @@ -# run benchmark: make sure proxy and server all are running; run this script from client -if [ -z "${MONOLAKE_HOME+set}" ]; then - export MONOLAKE_HOME=$HOME/monolake -fi - -if [ -z "${MONOLAKE_BENCHMARK_PROXY_IP+set}" ]; then - export MONOLAKE_BENCHMARK_PROXY_IP=localhost -fi - -if [ -z "${MONOLAKE_BENCHMARK_SERVER_IP+set}" ]; then - export MONOLAKE_BENCHMARK_SERVER_IP=localhost -fi - -cd $HOME/wrk2 - -# http proxy for nginx -./wrk -d 1m -c 640 -t 64 -R 210000 --latency http://$MONOLAKE_BENCHMARK_PROXY_IP:8100/server2 > http-result-4c-nginx-tiny.txt -./wrk -d 1m -c 640 -t 64 -R 200000 --latency http://$MONOLAKE_BENCHMARK_PROXY_IP:8100/server3 > http-result-4c-nginx-small.txt -./wrk -d 1m -c 640 -t 64 -R 120000 --latency http://$MONOLAKE_BENCHMARK_PROXY_IP:8100/server4 > http-result-4c-nginx-medium.txt -./wrk -d 1m -c 640 -t 64 -R 10000 --latency http://$MONOLAKE_BENCHMARK_PROXY_IP:8100/server5 > http-result-4c-nginx-large.txt - -# ./wrk -d 1m -c 3500 -t 20 -R 31300 --latency http://$MONOLAKE_BENCHMARK_PROXY_IP:8100/server2 > http-result-4c-nginx-tiny.txt -# ./wrk -d 1m -c 3500 -t 20 -R 30000 --latency http://$MONOLAKE_BENCHMARK_PROXY_IP:8100/server3 > http-result-4c-nginx-small.txt -# ./wrk -d 1m -c 3500 -t 20 -R 28800 --latency http://$MONOLAKE_BENCHMARK_PROXY_IP:8100/server4 > http-result-4c-nginx-medium.txt -# ./wrk -d 1m -c 1200 -t 20 -R 7500 --latency http://$MONOLAKE_BENCHMARK_PROXY_IP:8100/server5 > http-result-4c-nginx-large.txt - -# ./wrk -d 1m -c 1447 -t 20 -R 16000 --latency http://$MONOLAKE_BENCHMARK_PROXY_IP:8100/server2 > http-result-4c-nginx-tiny.txt -# ./wrk -d 1m -c 1447 -t 20 -R 20000 --latency http://$MONOLAKE_BENCHMARK_PROXY_IP:8100/server3 > http-result-4c-nginx-small.txt -# ./wrk -d 1m -c 1447 -t 20 -R 16000 --latency http://$MONOLAKE_BENCHMARK_PROXY_IP:8100/server4 > http-result-4c-nginx-medium.txt -# ./wrk -d 1m -c 1200 -t 20 -R 4000 --latency http://$MONOLAKE_BENCHMARK_PROXY_IP:8100/server5 > http-result-4c-nginx-large.txt diff --git a/benchmark/client/benchmark-nginx-https.sh b/benchmark/client/benchmark-nginx-https.sh deleted file mode 100755 index d907318..0000000 --- a/benchmark/client/benchmark-nginx-https.sh +++ /dev/null @@ -1,30 +0,0 @@ -# run benchmark: make sure proxy and server all are running; run this script from client -if [ -z "${MONOLAKE_HOME+set}" ]; then - export MONOLAKE_HOME=$HOME/monolake -fi - -if [ -z "${MONOLAKE_BENCHMARK_PROXY_IP+set}" ]; then - export MONOLAKE_BENCHMARK_PROXY_IP=localhost -fi - -if [ -z "${MONOLAKE_BENCHMARK_SERVER_IP+set}" ]; then - export MONOLAKE_BENCHMARK_SERVER_IP=localhost -fi - -cd $HOME/wrk2 - -# https proxy for nginx -./wrk -d 1m -c 640 -t 64 -R 150000 --latency https://$MONOLAKE_BENCHMARK_PROXY_IP:8443/server2 > https-result-4c-nginx-tiny.txt -./wrk -d 1m -c 640 -t 64 -R 140000 --latency https://$MONOLAKE_BENCHMARK_PROXY_IP:8443/server3 > https-result-4c-nginx-small.txt -./wrk -d 1m -c 640 -t 64 -R 80000 --latency https://$MONOLAKE_BENCHMARK_PROXY_IP:8443/server4 > https-result-4c-nginx-medium.txt -./wrk -d 1m -c 640 -t 64 -R 10000 --latency https://$MONOLAKE_BENCHMARK_PROXY_IP:8443/server5 > https-result-4c-nginx-large.txt - -# ./wrk -d 1m -c 3500 -t 20 -R 27000 --latency https://$MONOLAKE_BENCHMARK_PROXY_IP:8443/server2 > https-result-4c-nginx-tiny.txt -# ./wrk -d 1m -c 3500 -t 20 -R 26000 --latency https://$MONOLAKE_BENCHMARK_PROXY_IP:8443/server3 > https-result-4c-nginx-small.txt -# ./wrk -d 1m -c 3500 -t 20 -R 23000 --latency https://$MONOLAKE_BENCHMARK_PROXY_IP:8443/server4 > https-result-4c-nginx-medium.txt -# ./wrk -d 1m -c 3500 -t 20 -R 4500 --latency https://$MONOLAKE_BENCHMARK_PROXY_IP:8443/server5 > https-result-4c-nginx-large.txt - -# ./wrk -d 1m -c 1300 -t 20 -R 5000 --latency https://$MONOLAKE_BENCHMARK_PROXY_IP:8443/server2 > https-result-4c-nginx-tiny.txt -# ./wrk -d 1m -c 1300 -t 20 -R 5000 --latency https://$MONOLAKE_BENCHMARK_PROXY_IP:8443/server3 > https-result-4c-nginx-small.txt -# ./wrk -d 1m -c 1300 -t 20 -R 5000 --latency https://$MONOLAKE_BENCHMARK_PROXY_IP:8443/server4 > https-result-4c-nginx-medium.txt -# ./wrk -d 1m -c 1000 -t 20 -R 1800 --latency https://$MONOLAKE_BENCHMARK_PROXY_IP:8443/server5 > https-result-4c-nginx-large.txt diff --git a/benchmark/client/benchmark-traefik-16core-http.sh b/benchmark/client/benchmark-traefik-16core-http.sh deleted file mode 100755 index 9982b9d..0000000 --- a/benchmark/client/benchmark-traefik-16core-http.sh +++ /dev/null @@ -1,20 +0,0 @@ -# run benchmark: make sure proxy and server all are running; run this script from client -if [ -z "${MONOLAKE_HOME+set}" ]; then - export MONOLAKE_HOME=$HOME/monolake -fi - -if [ -z "${MONOLAKE_BENCHMARK_PROXY_IP+set}" ]; then - export MONOLAKE_BENCHMARK_PROXY_IP=localhost -fi - -if [ -z "${MONOLAKE_BENCHMARK_SERVER_IP+set}" ]; then - export MONOLAKE_BENCHMARK_SERVER_IP=localhost -fi - -cd $HOME/wrk2 - -# http proxy for traefik -./wrk -d 1m -c 1000 -t 20 -R 2000 --latency http://$MONOLAKE_BENCHMARK_PROXY_IP:8300/server2 > http-result-16c-traefik-tiny.txt -./wrk -d 1m -c 1000 -t 20 -R 2000 --latency http://$MONOLAKE_BENCHMARK_PROXY_IP:8300/server3 > http-result-16c-traefik-small.txt -./wrk -d 1m -c 1000 -t 20 -R 2000 --latency http://$MONOLAKE_BENCHMARK_PROXY_IP:8300/server4 > http-result-16c-traefik-medium.txt -./wrk -d 1m -c 1000 -t 20 -R 2000 --latency http://$MONOLAKE_BENCHMARK_PROXY_IP:8300/server5 > http-result-16c-traefik-large.txt diff --git a/benchmark/client/benchmark-traefik-16core-https.sh b/benchmark/client/benchmark-traefik-16core-https.sh deleted file mode 100755 index eea90ec..0000000 --- a/benchmark/client/benchmark-traefik-16core-https.sh +++ /dev/null @@ -1,20 +0,0 @@ -# run benchmark: make sure proxy and server all are running; run this script from client -if [ -z "${MONOLAKE_HOME+set}" ]; then - export MONOLAKE_HOME=$HOME/monolake -fi - -if [ -z "${MONOLAKE_BENCHMARK_PROXY_IP+set}" ]; then - export MONOLAKE_BENCHMARK_PROXY_IP=localhost -fi - -if [ -z "${MONOLAKE_BENCHMARK_SERVER_IP+set}" ]; then - export MONOLAKE_BENCHMARK_SERVER_IP=localhost -fi - -cd $HOME/wrk2 - -# https proxy for traefik -./wrk -d 1m -c 1000 -t 20 -R 2000 --latency https://$MONOLAKE_BENCHMARK_PROXY_IP:9443/server2 > https-result-16c-traefik-tiny.txt -./wrk -d 1m -c 1000 -t 20 -R 2000 --latency https://$MONOLAKE_BENCHMARK_PROXY_IP:9443/server3 > https-result-16c-traefik-small.txt -./wrk -d 1m -c 1000 -t 20 -R 2000 --latency https://$MONOLAKE_BENCHMARK_PROXY_IP:9443/server4 > https-result-16c-traefik-medium.txt -./wrk -d 1m -c 1000 -t 20 -R 2000 --latency https://$MONOLAKE_BENCHMARK_PROXY_IP:9443/server5 > https-result-16c-traefik-large.txt diff --git a/benchmark/client/benchmark-traefik-http.sh b/benchmark/client/benchmark-traefik-http.sh deleted file mode 100755 index 3d89252..0000000 --- a/benchmark/client/benchmark-traefik-http.sh +++ /dev/null @@ -1,30 +0,0 @@ -# run benchmark: make sure proxy and server all are running; run this script from client -if [ -z "${MONOLAKE_HOME+set}" ]; then - export MONOLAKE_HOME=$HOME/monolake -fi - -if [ -z "${MONOLAKE_BENCHMARK_PROXY_IP+set}" ]; then - export MONOLAKE_BENCHMARK_PROXY_IP=localhost -fi - -if [ -z "${MONOLAKE_BENCHMARK_SERVER_IP+set}" ]; then - export MONOLAKE_BENCHMARK_SERVER_IP=localhost -fi - -cd $HOME/wrk2 - -# http proxy for traefik -./wrk -d 1m -c 640 -t 64 -R 10000 --latency http://$MONOLAKE_BENCHMARK_PROXY_IP:8300/server2 > http-result-4c-traefik-tiny.txt -./wrk -d 1m -c 640 -t 64 -R 11000 --latency http://$MONOLAKE_BENCHMARK_PROXY_IP:8300/server3 > http-result-4c-traefik-small.txt -./wrk -d 1m -c 640 -t 64 -R 12000 --latency http://$MONOLAKE_BENCHMARK_PROXY_IP:8300/server4 > http-result-4c-traefik-medium.txt -./wrk -d 1m -c 640 -t 64 -R 12000 --latency http://$MONOLAKE_BENCHMARK_PROXY_IP:8300/server5 > http-result-4c-traefik-large.txt - -# ./wrk -d 1m -c 1447 -t 20 -R 1800 --latency http://$MONOLAKE_BENCHMARK_PROXY_IP:8300/server2 > http-result-4c-traefik-tiny.txt -# ./wrk -d 1m -c 1447 -t 20 -R 1800 --latency http://$MONOLAKE_BENCHMARK_PROXY_IP:8300/server3 > http-result-4c-traefik-small.txt -# ./wrk -d 1m -c 1447 -t 20 -R 1800 --latency http://$MONOLAKE_BENCHMARK_PROXY_IP:8300/server4 > http-result-4c-traefik-medium.txt -# ./wrk -d 1m -c 1447 -t 20 -R 6000 --latency http://$MONOLAKE_BENCHMARK_PROXY_IP:8300/server5 > http-result-4c-traefik-large.txt - -# ./wrk -d 1m -c 1447 -t 20 -R 16000 --latency http://$MONOLAKE_BENCHMARK_PROXY_IP:8300/server2 > http-result-4c-traefik-tiny.txt -# ./wrk -d 1m -c 1447 -t 20 -R 20000 --latency http://$MONOLAKE_BENCHMARK_PROXY_IP:8300/server3 > http-result-4c-traefik-small.txt -# ./wrk -d 1m -c 1447 -t 20 -R 16000 --latency http://$MONOLAKE_BENCHMARK_PROXY_IP:8300/server4 > http-result-4c-traefik-medium.txt -# ./wrk -d 1m -c 1200 -t 20 -R 4000 --latency http://$MONOLAKE_BENCHMARK_PROXY_IP:8300/server5 > http-result-4c-traefik-large.txt diff --git a/benchmark/client/benchmark-traefik-https.sh b/benchmark/client/benchmark-traefik-https.sh deleted file mode 100755 index 0b278c1..0000000 --- a/benchmark/client/benchmark-traefik-https.sh +++ /dev/null @@ -1,30 +0,0 @@ -# run benchmark: make sure proxy and server all are running; run this script from client -if [ -z "${MONOLAKE_HOME+set}" ]; then - export MONOLAKE_HOME=$HOME/monolake -fi - -if [ -z "${MONOLAKE_BENCHMARK_PROXY_IP+set}" ]; then - export MONOLAKE_BENCHMARK_PROXY_IP=localhost -fi - -if [ -z "${MONOLAKE_BENCHMARK_SERVER_IP+set}" ]; then - export MONOLAKE_BENCHMARK_SERVER_IP=localhost -fi - -cd $HOME/wrk2 - -# https proxy for traefik -./wrk -d 1m -c 640 -t 64 -R 10000 --latency https://$MONOLAKE_BENCHMARK_PROXY_IP:9443/server2 > https-result-4c-traefik-tiny.txt -./wrk -d 1m -c 640 -t 64 -R 12000 --latency https://$MONOLAKE_BENCHMARK_PROXY_IP:9443/server3 > https-result-4c-traefik-small.txt -./wrk -d 1m -c 640 -t 64 -R 14000 --latency https://$MONOLAKE_BENCHMARK_PROXY_IP:9443/server4 > https-result-4c-traefik-medium.txt -./wrk -d 1m -c 640 -t 64 -R 11000 --latency https://$MONOLAKE_BENCHMARK_PROXY_IP:9443/server5 > https-result-4c-traefik-large.txt - -# ./wrk -d 1m -c 1447 -t 20 -R 1800 --latency https://$MONOLAKE_BENCHMARK_PROXY_IP:9443/server2 > https-result-4c-traefik-tiny.txt -# ./wrk -d 1m -c 1447 -t 20 -R 1800 --latency https://$MONOLAKE_BENCHMARK_PROXY_IP:9443/server3 > https-result-4c-traefik-small.txt -# ./wrk -d 1m -c 1447 -t 20 -R 2800 --latency https://$MONOLAKE_BENCHMARK_PROXY_IP:9443/server4 > https-result-4c-traefik-medium.txt -# ./wrk -d 1m -c 1200 -t 20 -R 3600 --latency https://$MONOLAKE_BENCHMARK_PROXY_IP:9443/server5 > https-result-4c-traefik-large.txt - -# ./wrk -d 1m -c 1500 -t 20 -R 20000 --latency https://$MONOLAKE_BENCHMARK_PROXY_IP:9443/server2 > https-result-4c-traefik-tiny.txt -# ./wrk -d 1m -c 1500 -t 20 -R 20000 --latency https://$MONOLAKE_BENCHMARK_PROXY_IP:9443/server3 > https-result-4c-traefik-small.txt -# ./wrk -d 1m -c 1500 -t 20 -R 20000 --latency https://$MONOLAKE_BENCHMARK_PROXY_IP:9443/server4 > https-result-4c-traefik-medium.txt -# ./wrk -d 1m -c 1500 -t 20 -R 20000 --latency https://$MONOLAKE_BENCHMARK_PROXY_IP:9443/server5 > https-result-4c-traefik-large.txt diff --git a/benchmark/client/setup-once.sh b/benchmark/client/setup-once.sh deleted file mode 100755 index cef869f..0000000 --- a/benchmark/client/setup-once.sh +++ /dev/null @@ -1,14 +0,0 @@ -if [ -z "${MONOLAKE_HOME+set}" ]; then - export MONOLAKE_HOME=$HOME/monolake -fi - -cd $MONOLAKE_HOME/client -sudo yum -y install gcc git openssl-devel zlib-devel - -# download curl: it is installed by default - -# download wrk2 -cd $HOME -git clone https://github.com/giltene/wrk2 -cd wrk2 -make WITH_OPENSSL=/usr diff --git a/benchmark/client/verify-setup.sh b/benchmark/client/verify-setup.sh deleted file mode 100755 index 3710f8c..0000000 --- a/benchmark/client/verify-setup.sh +++ /dev/null @@ -1,35 +0,0 @@ -# verify if proxy and server are ready and running; run this dcript from client - -#export MONOLAKE_BENCHMARK_PROXY_IP=ec2-52-15-182-194.us-east-2.compute.amazonaws.com -#export MONOLAKE_BENCHMARK_SERVER_IP=ec2-3-129-244-251.us-east-2.compute.amazonaws.com - -if [ -z "${MONOLAKE_BENCHMARK_PROXY_IP+set}" ]; then - export MONOLAKE_BENCHMARK_PROXY_IP=localhost -fi - -if [ -z "${MONOLAKE_BENCHMARK_SERVER_IP+set}" ]; then - export MONOLAKE_BENCHMARK_SERVER_IP=localhost -fi - -# verify server is ready -curl -k http://$MONOLAKE_BENCHMARK_SERVER_IP:10082 - -# verify (nginx) proxy is ready -curl -k http://$MONOLAKE_BENCHMARK_PROXY_IP:8100/server2 -# curl -k http://$MONOLAKE_BENCHMARK_PROXY_IP:8200 -# verify (traefik) proxy is ready -curl -k http://$MONOLAKE_BENCHMARK_PROXY_IP:8300/server2 -# verify (monolake) proxy is ready -curl -k http://$MONOLAKE_BENCHMARK_PROXY_IP:8402 -# verify (envoy) proxy is ready -curl -k http://$MONOLAKE_BENCHMARK_PROXY_IP:8500/server2/ - -# verify (nginx) tls proxy is ready -curl -k https://$MONOLAKE_BENCHMARK_PROXY_IP:8443/server2 -# curl -k https://$MONOLAKE_BENCHMARK_PROXY_IP:7443 -# verify (traefik) tls proxy is ready -curl -k https://$MONOLAKE_BENCHMARK_PROXY_IP:9443/server2 -# verify (monolake) tls proxy is ready -curl -k https://$MONOLAKE_BENCHMARK_PROXY_IP:6442 -# verify (envoy) tls proxy is ready -curl -k https://$MONOLAKE_BENCHMARK_PROXY_IP:5443/server2/ diff --git a/benchmark/images/README/all-http-large-latency.png b/benchmark/images/README/all-http-large-latency.png deleted file mode 100644 index 1795647..0000000 Binary files a/benchmark/images/README/all-http-large-latency.png and /dev/null differ diff --git a/benchmark/images/README/all-http-latency.png 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diff --git a/benchmark/images/README/small-throughput.png b/benchmark/images/README/small-throughput.png deleted file mode 100644 index e32be3d..0000000 Binary files a/benchmark/images/README/small-throughput.png and /dev/null differ diff --git a/benchmark/images/README/tiny-qps.png b/benchmark/images/README/tiny-qps.png deleted file mode 100644 index bbf8ab0..0000000 Binary files a/benchmark/images/README/tiny-qps.png and /dev/null differ diff --git a/benchmark/images/README/tiny-throughput.png b/benchmark/images/README/tiny-throughput.png deleted file mode 100644 index dfd9283..0000000 Binary files a/benchmark/images/README/tiny-throughput.png and /dev/null differ diff --git a/benchmark/performance-collect.sh b/benchmark/performance-collect.sh deleted file mode 100755 index 024ca07..0000000 --- a/benchmark/performance-collect.sh +++ /dev/null @@ -1,60 +0,0 @@ -#!/bin/bash - -# process id to monitor -p_name=$1 -pid=`pidof $p_name | awk 'NF>1{print $NF}'` -if [ -z $pid ]; then - pid=`/bin/ps -A | grep "$p_name" | grep -v "grep" | awk '{print $1}'` - # pid=`/bin/ps -fu $USER| grep "$p_name" | grep -v "grep" | awk '{print $2}'` -fi -echo "pid=$pid" - -if [ -z $1 ]; then - echo "ERROR: Process not specified." - echo - echo "Usage: $(basename "$0") " - exit 1 -fi - -# check if process exists -kill -0 $pid > /dev/null 2>&1 -pid_exist=$? - -if [ $pid_exist != 0 ]; then - echo "ERROR: Process ID $pid not found." - exit 1 -fi - -current_time=$(date +"%Y_%m_%d_%H%M") -dir_name="." -csv_filename="${1}-performance.csv" - - -echo "Writing data to CSV file $csv_filename..." -touch $csv_filename - -# write CSV headers -echo "Time,CPU,Memory,TCP Connections,Thread Count" >> $csv_filename - -# check if process exists -kill -0 $pid > /dev/null 2>&1 -pid_exist=$? - -# collect until process exits -while [ $pid_exist == 0 ]; do - # check if process exists - kill -0 $pid > /dev/null 2>&1 - pid_exist=$? - - if [ $pid_exist == 0 ]; then - # read cpu and mem percentages - timestamp=$(date +"%b %d %H:%M:%S") - cpu_mem_usage=$(top -b -n 1 | grep -w -E "^ *$pid" | awk '{print $9 "," $10}') - tcp_cons=$(lsof -i -a -p $pid -w | tail -n +2 | wc -l) - tcount=$(ps -o nlwp h $pid | tr -d ' ') - - # write CSV row - echo "$timestamp,$cpu_mem_usage,$tcp_cons,$tcount" >> $csv_filename - sleep 1 - fi -done diff --git a/benchmark/pipeline-client-envoy.sh b/benchmark/pipeline-client-envoy.sh deleted file mode 100755 index fc5491d..0000000 --- a/benchmark/pipeline-client-envoy.sh +++ /dev/null @@ -1,13 +0,0 @@ -export client_url=3.133.229.116 -export proxy_url=18.217.152.113 -export server_url=3.22.140.218 -export proxy_private_url=172.31.2.253 -export server_private_url=172.31.22.170 - -# start client -client_cmd='cd ~/monolake/benchmark/client; export MONOLAKE_BENCHMARK_PROXY_IP=' -client_cmd+=$proxy_private_url -client_cmd+='; export MONOLAKE_BENCHMARK_SERVER_IP=' -client_cmd+=$server_private_url -client_cmd+='; ./benchmark-envoy-http.sh; ./benchmark-envoy-https.sh; echo "Please type exit to continue"; bash -l' -ssh -i $HOME/ssh/monolake-benchmark.pem ec2-user@${client_url} -t $client_cmd diff --git a/benchmark/pipeline-client-monolake.sh b/benchmark/pipeline-client-monolake.sh deleted file mode 100755 index 5c1118c..0000000 --- a/benchmark/pipeline-client-monolake.sh +++ /dev/null @@ -1,13 +0,0 @@ -export client_url=3.133.229.116 -export proxy_url=18.217.152.113 -export server_url=3.22.140.218 -export proxy_private_url=172.31.2.253 -export server_private_url=172.31.22.170 - -# start client -client_cmd='cd ~/monolake/benchmark/client; export MONOLAKE_BENCHMARK_PROXY_IP=' -client_cmd+=$proxy_private_url -client_cmd+='; export MONOLAKE_BENCHMARK_SERVER_IP=' -client_cmd+=$server_private_url -client_cmd+='; ./benchmark-monolake-http.sh; ./benchmark-monolake-https.sh; echo "Please type exit to continue"; bash -l' -ssh -i $HOME/ssh/monolake-benchmark.pem ec2-user@${client_url} -t $client_cmd diff --git a/benchmark/pipeline-client-nginx.sh b/benchmark/pipeline-client-nginx.sh deleted file mode 100755 index 5049337..0000000 --- a/benchmark/pipeline-client-nginx.sh +++ /dev/null @@ -1,13 +0,0 @@ -export client_url=3.133.229.116 -export proxy_url=18.217.152.113 -export server_url=3.22.140.218 -export proxy_private_url=172.31.2.253 -export server_private_url=172.31.22.170 - -# start client -client_cmd='cd ~/monolake/benchmark/client; export MONOLAKE_BENCHMARK_PROXY_IP=' -client_cmd+=$proxy_private_url -client_cmd+='; export MONOLAKE_BENCHMARK_SERVER_IP=' -client_cmd+=$server_private_url -client_cmd+='; ./benchmark-nginx-http.sh; ./benchmark-nginx-https.sh; echo "Please type exit to continue"; bash -l' -ssh -i $HOME/ssh/monolake-benchmark.pem ec2-user@${client_url} -t $client_cmd diff --git a/benchmark/pipeline-client-traefik.sh b/benchmark/pipeline-client-traefik.sh deleted file mode 100755 index 00537b3..0000000 --- a/benchmark/pipeline-client-traefik.sh +++ /dev/null @@ -1,13 +0,0 @@ -export client_url=3.133.229.116 -export proxy_url=18.217.152.113 -export server_url=3.22.140.218 -export proxy_private_url=172.31.2.253 -export server_private_url=172.31.22.170 - -# start client -client_cmd='cd ~/monolake/benchmark/client; export MONOLAKE_BENCHMARK_PROXY_IP=' -client_cmd+=$proxy_private_url -client_cmd+='; export MONOLAKE_BENCHMARK_SERVER_IP=' -client_cmd+=$server_private_url -client_cmd+='; ./benchmark-traefik-http.sh; ./benchmark-traefik-https.sh; echo "Please type exit to continue"; bash -l' -ssh -i $HOME/ssh/monolake-benchmark.pem ec2-user@${client_url} -t $client_cmd diff --git a/benchmark/pipeline-proxy-envoy.sh b/benchmark/pipeline-proxy-envoy.sh deleted file mode 100755 index 770be31..0000000 --- a/benchmark/pipeline-proxy-envoy.sh +++ /dev/null @@ -1,16 +0,0 @@ -export client_url=3.133.229.116 -export proxy_url=18.217.152.113 -export server_url=3.22.140.218 -export proxy_private_url=172.31.2.253 -export server_private_url=172.31.22.170 - -#manual update proxy configurations -#ssh -i $HOME/ssh/monolake-benchmark.pem ec2-user@${proxy_url} -t 'cd ~/monolake/benchmark/proxy; MONOLAKE_BENCHMARK_SERVER_IP=${server_url} ./update-server-ip.sh; bash -l' - -#then start proxy -proxy_cmd='export MONOLAKE_BENCHMARK_PROXY_IP=' -proxy_cmd+=$proxy_private_url -proxy_cmd+='; export MONOLAKE_BENCHMARK_SERVER_IP=' -proxy_cmd+=$server_private_url -proxy_cmd+='; ~/monolake/benchmark/proxy/start-envoy.sh; sleep 3; rm -f ~/envoy-performance.csv; sudo ~/monolake/benchmark/performance-collect.sh envoy; echo "Please type exit to continue"; bash -l' -ssh -i $HOME/ssh/monolake-benchmark.pem ec2-user@${proxy_url} -t $proxy_cmd diff --git a/benchmark/pipeline-proxy-monolake.sh b/benchmark/pipeline-proxy-monolake.sh deleted file mode 100755 index aa3ac6b..0000000 --- a/benchmark/pipeline-proxy-monolake.sh +++ /dev/null @@ -1,16 +0,0 @@ -export client_url=3.133.229.116 -export proxy_url=18.217.152.113 -export server_url=3.22.140.218 -export proxy_private_url=172.31.2.253 -export server_private_url=172.31.22.170 - -#manual update proxy configurations -#ssh -i $HOME/ssh/monolake-benchmark.pem ec2-user@${proxy_url} -t 'cd ~/monolake/benchmark/proxy; MONOLAKE_BENCHMARK_SERVER_IP=${server_url} ./update-server-ip.sh; bash -l' - -#then start proxy -proxy_cmd='export MONOLAKE_BENCHMARK_PROXY_IP=' -proxy_cmd+=$proxy_private_url -proxy_cmd+='; export MONOLAKE_BENCHMARK_SERVER_IP=' -proxy_cmd+=$server_private_url -proxy_cmd+='; ~/monolake/benchmark/proxy/start-monolake.sh; sleep 3; rm -f ~/monolake-performance.csv; ~/monolake/benchmark/performance-collect.sh monolake; echo "Please type exit to continue"; bash -l' -ssh -i $HOME/ssh/monolake-benchmark.pem ec2-user@${proxy_url} -t $proxy_cmd diff --git a/benchmark/pipeline-proxy-nginx.sh b/benchmark/pipeline-proxy-nginx.sh deleted file mode 100755 index 972cbd5..0000000 --- a/benchmark/pipeline-proxy-nginx.sh +++ /dev/null @@ -1,16 +0,0 @@ -export client_url=3.133.229.116 -export proxy_url=18.217.152.113 -export server_url=3.22.140.218 -export proxy_private_url=172.31.2.253 -export server_private_url=172.31.22.170 - -#manual update proxy configurations -#ssh -i $HOME/ssh/monolake-benchmark.pem ec2-user@${proxy_url} -t 'cd ~/monolake/benchmark/proxy; MONOLAKE_BENCHMARK_SERVER_IP=${server_url} ./update-server-ip.sh; bash -l' - -#then start proxy -proxy_cmd='export MONOLAKE_BENCHMARK_PROXY_IP=' -proxy_cmd+=$proxy_private_url -proxy_cmd+='; export MONOLAKE_BENCHMARK_SERVER_IP=' -proxy_cmd+=$server_private_url -proxy_cmd+='; ~/monolake/benchmark/proxy/start-nginx.sh; sleep 3; rm -f ~/nginx-performance.csv; sudo ~/monolake/benchmark/performance-collect.sh nginx; echo "Please type exit to continue"; bash -l' -ssh -i $HOME/ssh/monolake-benchmark.pem ec2-user@${proxy_url} -t $proxy_cmd diff --git a/benchmark/pipeline-proxy-traefik.sh b/benchmark/pipeline-proxy-traefik.sh deleted file mode 100755 index 2a46655..0000000 --- a/benchmark/pipeline-proxy-traefik.sh +++ /dev/null @@ -1,16 +0,0 @@ -export client_url=3.133.229.116 -export proxy_url=18.217.152.113 -export server_url=3.22.140.218 -export proxy_private_url=172.31.2.253 -export server_private_url=172.31.22.170 - -#manual update proxy configurations -#ssh -i $HOME/ssh/monolake-benchmark.pem ec2-user@${proxy_url} -t 'cd ~/monolake/benchmark/proxy; MONOLAKE_BENCHMARK_SERVER_IP=${server_url} ./update-server-ip.sh; bash -l' - -#then start proxy -proxy_cmd='export MONOLAKE_BENCHMARK_PROXY_IP=' -proxy_cmd+=$proxy_private_url -proxy_cmd+='; export MONOLAKE_BENCHMARK_SERVER_IP=' -proxy_cmd+=$server_private_url -proxy_cmd+='; ~/monolake/benchmark/proxy/start-traefik.sh; sleep 3; rm -f ~/traefik-performance.csv; ~/monolake/benchmark/performance-collect.sh traefik; echo "Please type exit to continue"; bash -l' -ssh -i $HOME/ssh/monolake-benchmark.pem ec2-user@${proxy_url} -t $proxy_cmd diff --git a/benchmark/pipeline-proxy.sh b/benchmark/pipeline-proxy.sh deleted file mode 100644 index 574a5d0..0000000 --- a/benchmark/pipeline-proxy.sh +++ /dev/null @@ -1,16 +0,0 @@ -export client_url=3.133.229.116 -export proxy_url=3.19.41.190 -export server_url=3.22.140.218 -export proxy_private_url=172.31.7.16 -export server_private_url=172.31.22.170 - -#manual update proxy configurations -#ssh -i $HOME/ssh/monolake-benchmark.pem ec2-user@${proxy_url} -t 'cd ~/monolake/benchmark/proxy; MONOLAKE_BENCHMARK_SERVER_IP=${server_url} ./update-server-ip.sh; bash -l' - -#then start proxy -proxy_cmd='export MONOLAKE_BENCHMARK_PROXY_IP=' -proxy_cmd+=$proxy_private_url -proxy_cmd+='; export MONOLAKE_BENCHMARK_SERVER_IP=' -proxy_cmd+=$server_private_url -proxy_cmd+='; ~/monolake/benchmark/proxy/start-nginx.sh; sleep 3; rm -f ~/nginx-performance.csv; sudo ~/monolake/benchmark/performance-collect.sh nginx; echo "Please type exit to continue"; bash -l' -ssh -i $HOME/ssh/monolake-benchmark.pem ec2-user@${proxy_url} -t $proxy_cmd diff --git a/benchmark/pipeline-server.sh b/benchmark/pipeline-server.sh deleted file mode 100755 index 852c957..0000000 --- a/benchmark/pipeline-server.sh +++ /dev/null @@ -1,9 +0,0 @@ -export client_url=3.133.229.116 -export proxy_url=18.217.152.113 -export server_url=3.22.140.218 -export proxy_private_url=172.31.2.253 -export server_private_url=172.31.22.170 - -# start server -server_cmd='sudo rm -f /var/log/nginx/error.log /var/log/nginx/access.log; sudo service nginx restart; sleep 3; sudo rm -f nginx-performance.csv; sudo ~/monolake/benchmark/performance-collect.sh nginx; echo "Please type exit to continue"; bash -l' -ssh -i $HOME/ssh/monolake-benchmark.pem ec2-user@${server_url} -t $server_cmd diff --git a/benchmark/proxy/envoy/envoy.yaml b/benchmark/proxy/envoy/envoy.yaml deleted file mode 100644 index 39771d6..0000000 --- a/benchmark/proxy/envoy/envoy.yaml +++ /dev/null @@ -1,89 +0,0 @@ -static_resources: - listeners: - - address: - socket_address: { protocol: TCP, address: 0.0.0.0, port_value: 8500 } - filter_chains: - - filters: - - name: envoy.filters.network.http_connection_manager - typed_config: - "@type": type.googleapis.com/envoy.extensions.filters.network.http_connection_manager.v3.HttpConnectionManager - stat_prefix: ingress_http - codec_type: AUTO - generate_request_id: false - http_filters: - - name: envoy.filters.http.router - typed_config: - "@type": type.googleapis.com/envoy.extensions.filters.http.router.v3.Router - route_config: - name: local_route - virtual_hosts: - - name: local_service - domains: ["*"] - routes: - - match: - prefix: "/" - route: - cluster: some_service - - address: - socket_address: { protocol: TCP, address: 0.0.0.0, port_value: 5443 } - filter_chains: - - filters: - - name: envoy.filters.network.http_connection_manager - typed_config: - "@type": type.googleapis.com/envoy.extensions.filters.network.http_connection_manager.v3.HttpConnectionManager - stat_prefix: ingress_http - codec_type: AUTO - generate_request_id: false - http_filters: - - name: envoy.filters.http.router - typed_config: - "@type": type.googleapis.com/envoy.extensions.filters.http.router.v3.Router - route_config: - name: local_route - virtual_hosts: - - name: local_service - domains: ["*"] - routes: - - match: - prefix: "/" - route: - cluster: some_service - transport_socket: - name: envoy.transport_sockets.tls - typed_config: - "@type": type.googleapis.com/envoy.extensions.transport_sockets.tls.v3.DownstreamTlsContext - common_tls_context: - tls_certificates: - - certificate_chain: - filename: "cert.pem" - private_key: - filename: "cert.key" - clusters: - - name: some_service - connect_timeout: 30s - type: STATIC - lb_policy: ROUND_ROBIN - circuit_breakers: - thresholds: - - priority: DEFAULT - max_connections: 1000000000 - max_pending_requests: 1000000000 - max_requests: 1000000000 - max_retries: 1000000000 - - priority: HIGH - max_connections: 1000000000 - max_pending_requests: 1000000000 - max_requests: 1000000000 - max_retries: 1000000000 - load_assignment: - cluster_name: service_envoyproxy_io - endpoints: - - lb_endpoints: - - endpoint: - address: - socket_address: - address: 172.31.22.170 - port_value: 10080 -stats_config: - stats_matcher: - reject_all: true diff --git a/benchmark/proxy/envoy/setup-once.sh b/benchmark/proxy/envoy/setup-once.sh deleted file mode 100755 index 21b1e10..0000000 --- a/benchmark/proxy/envoy/setup-once.sh +++ /dev/null @@ -1,12 +0,0 @@ -# setup envoy proxy - -if [ -z "${MONOLAKE_HOME+set}" ]; then - export MONOLAKE_HOME=$HOME/monolake -fi - -cd $MONOLAKE_HOME/benchmark/proxy/envoy -wget https://github.com/envoyproxy/envoy/releases/download/v1.31.0/envoy-1.31.0-linux-x86_64 -chmod +x envoy-1.31.0-linux-x86_64 -mv envoy-1.31.0-linux-x86_64 envoy -echo "Please fill all fields when generating OpenSSL certs." -sudo openssl req -x509 -nodes -days 365 -newkey rsa:2048 -keyout $MONOLAKE_HOME/benchmark/proxy/envoy/cert.key -out $MONOLAKE_HOME/benchmark/proxy/envoy/cert.pem diff --git a/benchmark/proxy/monolake/monolake.toml b/benchmark/proxy/monolake/monolake.toml deleted file mode 100644 index 3247b2d..0000000 --- a/benchmark/proxy/monolake/monolake.toml +++ /dev/null @@ -1,64 +0,0 @@ -[runtime] -# runtime_type = "legacy" -worker_threads = 4 -entries = 8192 - -[servers.server_basic2] - name = "monolake.cloudwego.io" - listener = { type = "socket", value = "0.0.0.0:8402" } - [[servers.server_basic2.routes]] - path = '/' - upstreams = [{ endpoint = { type = "uri", value = "http://127.0.0.1:10082" } }] - -[servers.server_basic3] - name = "monolake.cloudwego.io" - listener = { type = "socket", value = "0.0.0.0:8403" } - [[servers.server_basic3.routes]] - path = '/' - upstreams = [{ endpoint = { type = "uri", value = "http://127.0.0.1:10083" } }] - -[servers.server_basic4] - name = "monolake.cloudwego.io" - listener = { type = "socket", value = "0.0.0.0:8404" } - [[servers.server_basic4.routes]] - path = '/' - upstreams = [{ endpoint = { type = "uri", value = "http://127.0.0.1:10084" } }] - -[servers.server_basic5] - name = "monolake.cloudwego.io" - listener = { type = "socket", value = "0.0.0.0:8405" } - [[servers.server_basic5.routes]] - path = '/' - upstreams = [{ endpoint = { type = "uri", value = "http://127.0.0.1:10085" } }] - -[servers.server_tls2] - tls = { chain = "examples/certs/cert.pem", key = "examples/certs/key.pem" } - name = "monolake.cloudwego.io" - listener = { type = "socket", value = "0.0.0.0:6442" } - [[servers.server_tls2.routes]] - path = '/' - upstreams = [{ endpoint = { type = "uri", value = "http://127.0.0.1:10082" } }] - -[servers.server_tls3] - tls = { chain = "examples/certs/cert.pem", key = "examples/certs/key.pem" } - name = "monolake.cloudwego.io" - listener = { type = "socket", value = "0.0.0.0:6443" } - [[servers.server_tls3.routes]] - path = '/' - upstreams = [{ endpoint = { type = "uri", value = "http://127.0.0.1:10083" } }] - -[servers.server_tls4] - tls = { chain = "examples/certs/cert.pem", key = "examples/certs/key.pem" } - name = "monolake.cloudwego.io" - listener = { type = "socket", value = "0.0.0.0:6444" } - [[servers.server_tls4.routes]] - path = '/' - upstreams = [{ endpoint = { type = "uri", value = "http://127.0.0.1:10084" } }] - -[servers.server_tls5] - tls = { chain = "examples/certs/cert.pem", key = "examples/certs/key.pem" } - name = "monolake.cloudwego.io" - listener = { type = "socket", value = "0.0.0.0:6445" } - [[servers.server_tls5.routes]] - path = '/' - upstreams = [{ endpoint = { type = "uri", value = "http://127.0.0.1:10085" } }] diff --git a/benchmark/proxy/monolake/setup-once.sh b/benchmark/proxy/monolake/setup-once.sh deleted file mode 100755 index 95373ef..0000000 --- a/benchmark/proxy/monolake/setup-once.sh +++ /dev/null @@ -1,25 +0,0 @@ -# setup monolake proxy - -if [ -z "${MONOLAKE_HOME+set}" ]; then - export MONOLAKE_HOME=$HOME/monolake -fi - -cd $HOME - -sudo yum -y install gcc openssl-devel - -# install rust nightly -curl --proto '=https' --tlsv1.2 -sSf https://sh.rustup.rs | sh -. "$HOME/.cargo/env" -rustup toolchain install nightly -rustup default nightly - -cd $MONOLAKE_HOME - -# generate certs -sh -c "cd examples && ./gen_cert.sh" -mkdir -p examples/certs && openssl req -x509 -newkey rsa:2048 -keyout examples/certs/key.pem -out examples/certs/cert.pem -sha256 -days 365 -nodes -subj "/CN=monolake.cloudwego.io" - -# build monolake -cd $MONOLAKE_HOME -cargo build --release diff --git a/benchmark/proxy/nginx/nginx.conf b/benchmark/proxy/nginx/nginx.conf deleted file mode 100644 index 02dac55..0000000 --- a/benchmark/proxy/nginx/nginx.conf +++ /dev/null @@ -1,190 +0,0 @@ -#user nobody; -worker_processes 4; - -worker_rlimit_nofile 1000000; - -error_log /var/log/nginx/error.log crit; - -#pid logs/nginx.pid; - -events { - # determines how much clients will be served per worker - # max clients = worker_connections * worker_processes - # max clients is also limited by the number of socket connections available on the system (~64k) - worker_connections 4000; - - # optimized to serve many clients with each thread, essential for linux -- for testing environment - #use epoll; - - # accept as many connections as possible, may flood worker connections if set too low -- for testing environment - #multi_accept on; -} - - -http { - include /etc/nginx/mime.types; - default_type application/octet-stream; - # types_hash_bucket_size 128; - types_hash_max_size 32768; - - # cache informations about FDs, frequently accessed files - # can boost performance, but you need to test those values - #open_file_cache max=200000 inactive=20s; - #open_file_cache_valid 30s; - #open_file_cache_min_uses 2; - #open_file_cache_errors on; - - # to boost I/O on HDD we can disable access logs - access_log off; - - error_log off; - #client_body_buffer_size 10K; - #client_header_buffer_size 1k; - #client_max_body_size 8m; - #large_client_header_buffers 2 1k; - - # copies data between one FD and other from within the kernel - # faster than read() + write() - sendfile on; - - # send headers in one piece, it is better than sending them one by one - tcp_nopush on; - - # don't buffer data sent, good for small data bursts in real time - # https://brooker.co.za/blog/2024/05/09/nagle.html - # https://news.ycombinator.com/item?id=10608356 - #tcp_nodelay on; - - # allow the server to close connection on non responding client, this will free up memory - #reset_timedout_connection on; - - # request timed out -- default 60 - #client_body_timeout 10; - - # if client stop responding, free up memory -- default 60 - #send_timeout 2; - - # server will close connection after this time -- default 75 - keepalive_timeout 75; - - # number of requests client can make over keep-alive -- for testing environment - #keepalive_requests 100000; - keepalive_requests 1000000000; - - #gzip on; - - # Upstreams for https - upstream ssl_file_server_com2 { - server 127.0.0.1:10082; - keepalive 1024; - } - - upstream ssl_file_server_com3 { - server 127.0.0.1:10083; - keepalive 1024; - } - - upstream ssl_file_server_com4 { - server 127.0.0.1:10084; - keepalive 1024; - } - - upstream ssl_file_server_com5 { - server 127.0.0.1:10085; - keepalive 1024; - } - - server { - listen 8100; - server_name 127.0.0.1; - - #charset koi8-r; - - access_log off; - - location / { - root html; - index index.html index.htm; - } - - #error_page 404 /404.html; - - # redirect server error pages to the static page /50x.html - # - error_page 500 502 503 504 /50x.html; - location = /50x.html { - root html; - } - location /server2 { - proxy_pass http://ssl_file_server_com2/; - proxy_http_version 1.1; - proxy_set_header Connection ""; - } - location /server3 { - proxy_pass http://ssl_file_server_com3/; - proxy_http_version 1.1; - proxy_set_header Connection ""; - } - location /server4 { - proxy_pass http://ssl_file_server_com4/; - proxy_http_version 1.1; - proxy_set_header Connection ""; - } - location /server5 { - proxy_pass http://ssl_file_server_com5/; - proxy_http_version 1.1; - proxy_set_header Connection ""; - } - } - - # HTTPS server - # - server { - listen 8443 ssl; - server_name 127.0.0.1; - access_log off; - - ssl_certificate /etc/nginx/cert.pem; - ssl_certificate_key /etc/nginx/cert.key; - - ssl_session_cache shared:SSL:1m; - ssl_session_timeout 5m; - - ssl_ciphers HIGH:!aNULL:!MD5; - ssl_prefer_server_ciphers on; - - # location / { - # root html; - # index index.html index.htm; - # } - - location /server2 { - proxy_buffering off; - proxy_pass http://ssl_file_server_com2/; - proxy_http_version 1.1; - proxy_set_header Connection ""; - } - - location /server3 { - proxy_buffering off; - proxy_pass http://ssl_file_server_com3/; - proxy_http_version 1.1; - proxy_set_header Connection ""; - } - - location /server4 { - proxy_buffering off; - proxy_pass http://ssl_file_server_com4/; - proxy_http_version 1.1; - proxy_set_header Connection ""; - } - - location /server5 { - proxy_buffering off; - proxy_pass http://ssl_file_server_com5/; - proxy_http_version 1.1; - proxy_set_header Connection ""; - } - } - include servers/*; -} diff --git a/benchmark/proxy/nginx/setup-once.sh b/benchmark/proxy/nginx/setup-once.sh deleted file mode 100755 index 040a898..0000000 --- a/benchmark/proxy/nginx/setup-once.sh +++ /dev/null @@ -1,23 +0,0 @@ -# setup nginx proxy - -if [ -z "${MONOLAKE_HOME+set}" ]; then - export MONOLAKE_HOME=$HOME/monolake -fi - -# build nginx -# sudo yum -y install pcre pcre-devel zlib-devel -# wget https://nginx.org/download/nginx-1.27.1.tar.gz -# tar -zxvf nginx-1.27.1.tar.gz -# cd nginx-1.27.1 -# ./configure --with-http_ssl_module -# make -# sudo make install -# installed to: /usr/local/nginx/sbin/nginx - -sudo yum install -y nginx -sudo openssl req -x509 -nodes -days 365 -newkey rsa:2048 -keyout /etc/nginx/cert.key -out /etc/nginx/cert.pem -sudo cat /etc/nginx/cert.key /etc/nginx/cert.pem > $MONOLAKE_HOME/combined.pem -sudo mv $MONOLAKE_HOME/combined.pem /etc/nginx/ -sudo cp /etc/nginx/cert.key $MONOLAKE_HOME/examples/certs/key.pem -sudo cp /etc/nginx/cert.pem $MONOLAKE_HOME/examples/certs/ -sudo chmod 777 $MONOLAKE_HOME/examples/certs/key.pem $MONOLAKE_HOME/examples/certs/cert.pem diff --git a/benchmark/proxy/setup-once.sh b/benchmark/proxy/setup-once.sh deleted file mode 100755 index b1dcbe7..0000000 --- a/benchmark/proxy/setup-once.sh +++ /dev/null @@ -1,11 +0,0 @@ -# start nginx server - -if [ -z "${MONOLAKE_HOME+set}" ]; then - export MONOLAKE_HOME=$HOME/monolake -fi - -cd $MONOLAKE_HOME/benchmark/proxy -./monolake/setup-once.sh -./nginx/setup-once.sh -./traefik/setup-once.sh -./envoy/setup-once.sh diff --git a/benchmark/proxy/start-envoy.sh b/benchmark/proxy/start-envoy.sh deleted file mode 100755 index 890ca35..0000000 --- a/benchmark/proxy/start-envoy.sh +++ /dev/null @@ -1,7 +0,0 @@ -# start envoy proxy service -if [ -z "${MONOLAKE_HOME+set}" ]; then - export MONOLAKE_HOME=$HOME/monolake -fi - -cd $MONOLAKE_HOME/benchmark/proxy/envoy -sudo $MONOLAKE_HOME/benchmark/proxy/envoy/envoy -c $MONOLAKE_HOME/benchmark/proxy/envoy/envoy.yaml & diff --git a/benchmark/proxy/start-monolake.sh b/benchmark/proxy/start-monolake.sh deleted file mode 100755 index e30c0b8..0000000 --- a/benchmark/proxy/start-monolake.sh +++ /dev/null @@ -1,8 +0,0 @@ -# start monolake proxy service - -if [ -z "${MONOLAKE_HOME+set}" ]; then - export MONOLAKE_HOME=$HOME/monolake -fi - -cd $MONOLAKE_HOME -RUST_LOG=none target/release/monolake -c benchmark/proxy/monolake/monolake.toml & diff --git a/benchmark/proxy/start-nginx.sh b/benchmark/proxy/start-nginx.sh deleted file mode 100755 index c40c108..0000000 --- a/benchmark/proxy/start-nginx.sh +++ /dev/null @@ -1,8 +0,0 @@ -# start nginx proxy service -if [ -z "${MONOLAKE_HOME+set}" ]; then - export MONOLAKE_HOME=$HOME/monolake -fi - -sudo rm -f /var/log/nginx/error.log /var/log/nginx/access.log - -sudo /usr/sbin/nginx -c $MONOLAKE_HOME/benchmark/proxy/nginx/nginx.conf -g "pid /var/run/nginx2.pid;" & diff --git a/benchmark/proxy/start-traefik.sh b/benchmark/proxy/start-traefik.sh deleted file mode 100755 index 8676d39..0000000 --- a/benchmark/proxy/start-traefik.sh +++ /dev/null @@ -1,10 +0,0 @@ -# start traefik proxy service - -if [ -z "${MONOLAKE_HOME+set}" ]; then - export MONOLAKE_HOME=$HOME/monolake -fi - -cd $MONOLAKE_HOME/benchmark/proxy/traefik/ -rm -f *log* - -./traefik --configFile=traefik-static.toml & diff --git a/benchmark/proxy/stop-envoy.sh b/benchmark/proxy/stop-envoy.sh deleted file mode 100755 index 698e1d3..0000000 --- a/benchmark/proxy/stop-envoy.sh +++ /dev/null @@ -1,2 +0,0 @@ -# stop envoy proxy service -sudo kill -15 $(ps aux | grep 'envoy' | awk '{print $2}') diff --git a/benchmark/proxy/stop-monolake.sh b/benchmark/proxy/stop-monolake.sh deleted file mode 100755 index a1e9399..0000000 --- a/benchmark/proxy/stop-monolake.sh +++ /dev/null @@ -1,2 +0,0 @@ -# stop monolake proxy service -kill -15 $(ps aux | grep 'monolake' | awk '{print $2}') diff --git a/benchmark/proxy/stop-nginx.sh b/benchmark/proxy/stop-nginx.sh deleted file mode 100755 index aacc3ef..0000000 --- a/benchmark/proxy/stop-nginx.sh +++ /dev/null @@ -1,4 +0,0 @@ -# stop nginx proxy service -sudo kill -15 $(ps aux | grep 'nginx' | awk '{print $2}') - -sudo rm -f /var/log/nginx/error.log /var/log/nginx/access.log diff --git a/benchmark/proxy/stop-traefik.sh b/benchmark/proxy/stop-traefik.sh deleted file mode 100755 index f83b430..0000000 --- a/benchmark/proxy/stop-traefik.sh +++ /dev/null @@ -1,9 +0,0 @@ -# stop traefik proxy service -if [ -z "${MONOLAKE_HOME+set}" ]; then - export MONOLAKE_HOME=$HOME/monolake -fi - -kill -15 $(ps aux | grep 'traefik' | awk '{print $2}') - -cd $MONOLAKE_HOME/benchmark/proxy/traefik/ -rm -f *log* diff --git a/benchmark/proxy/traefik/setup-once.sh b/benchmark/proxy/traefik/setup-once.sh deleted file mode 100755 index 337ad72..0000000 --- a/benchmark/proxy/traefik/setup-once.sh +++ /dev/null @@ -1,10 +0,0 @@ -# setup traefik proxy - -if [ -z "${MONOLAKE_HOME+set}" ]; then - export MONOLAKE_HOME=$HOME/monolake -fi - -cd $MONOLAKE_HOME/benchmark/proxy/traefik/ -wget https://github.com/traefik/traefik/releases/download/v3.0.0-rc1/traefik_v3.0.0-rc1_linux_amd64.tar.gz -tar zxvf traefik_v3.0.0-rc1_linux_amd64.tar.gz -rm traefik_v3.0.0-rc1_linux_amd64.tar.gz diff --git a/benchmark/proxy/traefik/traefik-dynamic.toml b/benchmark/proxy/traefik/traefik-dynamic.toml deleted file mode 100644 index 00486d2..0000000 --- a/benchmark/proxy/traefik/traefik-dynamic.toml +++ /dev/null @@ -1,84 +0,0 @@ -[http] - [http.middlewares] - [http.middlewares.server2-stripprefix.stripPrefix] - prefixes = ["/server2"] - [http.middlewares.server3-stripprefix.stripPrefix] - prefixes = ["/server3"] - [http.middlewares.server4-stripprefix.stripPrefix] - prefixes = ["/server4"] - [http.middlewares.server5-stripprefix.stripPrefix] - prefixes = ["/server5"] - - [http.routers] - [http.routers.my-router2] - rule = "Path(`/server2`)" - service = "my-service2" - entryPoints = ["web"] - middlewares = ["server2-stripprefix"] - - [http.routers.my-router3] - rule = "Path(`/server3`)" - service = "my-service3" - entryPoints = ["web"] - middlewares = ["server3-stripprefix"] - - [http.routers.my-router4] - rule = "Path(`/server4`)" - service = "my-service4" - entryPoints = ["web"] - middlewares = ["server4-stripprefix"] - - [http.routers.my-router5] - rule = "Path(`/server5`)" - service = "my-service5" - entryPoints = ["web"] - middlewares = ["server5-stripprefix"] - - [http.routers.my-router12] - rule = "Path(`/server2`)" - service = "my-service2" - entryPoints = ["web-secure"] - middlewares = ["server2-stripprefix"] - tls = true - - [http.routers.my-router13] - rule = "Path(`/server3`)" - service = "my-service3" - entryPoints = ["web-secure"] - middlewares = ["server3-stripprefix"] - tls = true - - [http.routers.my-router14] - rule = "Path(`/server4`)" - service = "my-service4" - entryPoints = ["web-secure"] - middlewares = ["server4-stripprefix"] - tls = true - - [http.routers.my-router15] - rule = "Path(`/server5`)" - service = "my-service5" - entryPoints = ["web-secure"] - middlewares = ["server5-stripprefix"] - tls = true - - [http.services] - [http.services.my-service2.loadBalancer] - passHostHeader = false - [[http.services.my-service2.loadBalancer.servers]] - url = "http://127.0.0.1:10082/" - - [http.services.my-service3.loadBalancer] - passHostHeader = false - [[http.services.my-service3.loadBalancer.servers]] - url = "http://127.0.0.1:10083/" - - [http.services.my-service4.loadBalancer] - passHostHeader = false - [[http.services.my-service4.loadBalancer.servers]] - url = "http://127.0.0.1:10084/" - - [http.services.my-service5.loadBalancer] - passHostHeader = false - [[http.services.my-service5.loadBalancer.servers]] - url = "http://127.0.0.1:10085/" diff --git a/benchmark/proxy/traefik/traefik-static.toml b/benchmark/proxy/traefik/traefik-static.toml deleted file mode 100644 index 556b4a5..0000000 --- a/benchmark/proxy/traefik/traefik-static.toml +++ /dev/null @@ -1,32 +0,0 @@ -[log] - level = "ERROR" - filePath = "log-file.log" - -[accessLog] - filePath = "log-access.log" - bufferingSize = 100 - -[providers] - [providers.file] - filename = "traefik-dynamic.toml" - -[api] - # insecure = true - # dashboard = false - debug = false - -[entryPoints] - [entryPoints.web] - address = ":8300" - [entryPoints.web-secure] - address = ":9443" - # [entryPoints.dashboard] - # address = ":8080" - -[certificatesResolvers.sample.acme] - email = "mymail@example.com" - storage = "acme.json" - -[certificatesResolvers.sample.acme.httpChallenge] - # used during the challenge - entryPoint = "web-secure" diff --git a/benchmark/proxy/update-server-ip.sh b/benchmark/proxy/update-server-ip.sh deleted file mode 100755 index 70f3ae2..0000000 --- a/benchmark/proxy/update-server-ip.sh +++ /dev/null @@ -1,20 +0,0 @@ -# replace server ip in proxy services config file - -if [ -z "${MONOLAKE_HOME+set}" ]; then - export MONOLAKE_HOME=$HOME/monolake -fi - -if [ -z "${MONOLAKE_BENCHMARK_PROXY_IP+set}" ]; then - export MONOLAKE_BENCHMARK_PROXY_IP=localhost -fi - -if [ -z "${MONOLAKE_BENCHMARK_SERVER_IP+set}" ]; then - export MONOLAKE_BENCHMARK_SERVER_IP=localhost -fi - -cd $MONOLAKE_HOME/benchmark/proxy -echo "please copy and paste following commands and run manually to replace server ip in proxy services config file" -echo "sed -i -e 's/127.0.0.1/${MONOLAKE_BENCHMARK_SERVER_IP}/g' nginx/nginx.conf" -echo "sed -i -e 's/127.0.0.1/${MONOLAKE_BENCHMARK_SERVER_IP}/g' monolake/monolake.toml" -echo "sed -i -e 's/127.0.0.1/${MONOLAKE_BENCHMARK_SERVER_IP}/g' traefik/traefik-dynamic.toml" -echo "sed -i -e 's/127.0.0.1/${MONOLAKE_BENCHMARK_SERVER_IP}/g' envoy/envoy.yaml" diff --git a/benchmark/server/nginx-web.conf b/benchmark/server/nginx-web.conf deleted file mode 100644 index 8c99c68..0000000 --- a/benchmark/server/nginx-web.conf +++ /dev/null @@ -1,189 +0,0 @@ -#user nobody; -worker_processes 4; -worker_rlimit_nofile 100000; - -error_log /var/log/nginx/error.log crit; - -#pid logs/nginx.pid; - -events { - # determines how much clients will be served per worker - # max clients = worker_connections * worker_processes - # max clients is also limited by the number of socket connections available on the system (~64k) - worker_connections 4000; - - # optimized to serve many clients with each thread, essential for linux -- for testing environment - use epoll; - - # accept as many connections as possible, may flood worker connections if set too low -- for testing environment - multi_accept on; -} - - -http { - include /etc/nginx/mime.types; - default_type application/octet-stream; - - # cache informations about FDs, frequently accessed files - # can boost performance, but you need to test those values - #open_file_cache max=200000 inactive=20s; - #open_file_cache_valid 30s; - #open_file_cache_min_uses 2; - #open_file_cache_errors on; - - # to boost I/O on HDD we can disable access logs - access_log off; - - # copies data between one FD and other from within the kernel - # faster than read() + write() - sendfile on; - - # send headers in one piece, it is better than sending them one by one - tcp_nopush on; - - # don't buffer data sent, good for small data bursts in real time - # https://brooker.co.za/blog/2024/05/09/nagle.html - # https://news.ycombinator.com/item?id=10608356 - #tcp_nodelay on; - - # allow the server to close connection on non responding client, this will free up memory - #reset_timedout_connection on; - - # request timed out -- default 60 - #client_body_timeout 10; - - # if client stop responding, free up memory -- default 60 - #send_timeout 2; - - # server will close connection after this time -- default 75 - keepalive_timeout 75; - - # number of requests client can make over keep-alive -- for testing environment - keepalive_requests 1000000000; - - #gzip on; - - server { - listen 10082; - server_name localhost; - - #charset koi8-r; - - access_log off; - open_file_cache max=1000; - - location / { - root /usr/share/nginx/html/server2; - index index.html index.htm; - } - - #error_page 404 /404.html; - - # redirect server error pages to the static page /50x.html - # - error_page 500 502 503 504 /50x.html; - location = /50x.html { - root html; - } - - } - - server { - listen 10083; - server_name localhost; - - #charset koi8-r; - - access_log off; - open_file_cache max=1000; - - location / { - root /usr/share/nginx/html/server3; - index index.html index.htm; - } - - #error_page 404 /404.html; - - # redirect server error pages to the static page /50x.html - # - error_page 500 502 503 504 /50x.html; - location = /50x.html { - root html; - } - - } - - server { - listen 10084; - server_name localhost; - - #charset koi8-r; - - access_log off; - open_file_cache max=1000; - - location / { - root /usr/share/nginx/html/server4; - index index.html index.htm; - } - - #error_page 404 /404.html; - - # redirect server error pages to the static page /50x.html - # - error_page 500 502 503 504 /50x.html; - location = /50x.html { - root html; - } - - } - - server { - listen 10085; - server_name localhost; - - #charset koi8-r; - - access_log off; - open_file_cache max=1000; - - location / { - root /usr/share/nginx/html/server5; - index index.html index.htm; - } - - #error_page 404 /404.html; - - # redirect server error pages to the static page /50x.html - # - error_page 500 502 503 504 /50x.html; - location = /50x.html { - root html; - } - - } - - - # HTTPS server - # - # server { - # listen 8443 ssl; - # server_name localhost; - - # ssl_certificate cert.pem; - # ssl_certificate_key cert.key; - - # ssl_session_cache shared:SSL:1m; - # ssl_session_timeout 5m; - - # ssl_ciphers HIGH:!aNULL:!MD5; - # ssl_prefer_server_ciphers on; - - # location / { - # root html; - # index index.html index.htm; - # } - - # } - include servers/*; -} diff --git a/benchmark/server/setup-once.sh b/benchmark/server/setup-once.sh deleted file mode 100755 index 7ae0c82..0000000 --- a/benchmark/server/setup-once.sh +++ /dev/null @@ -1,14 +0,0 @@ -# start nginx server - -if [ -z "${MONOLAKE_HOME+set}" ]; then - export MONOLAKE_HOME=$HOME/monolake -fi - -sudo yum -y install nginx -sudo mv /etc/nginx/nginx.conf /etc/nginx/nginx-original.conf -sudo cp $MONOLAKE_HOME/benchmark/server/nginx-web.conf /etc/nginx/nginx.conf -sudo openssl req -x509 -nodes -days 365 -newkey rsa:2048 -keyout /etc/nginx/cert.key -out /etc/nginx/cert.pem -sudo cat /etc/nginx/cert.key /etc/nginx/cert.pem > $MONOLAKE_HOME/combined.pem -sudo mv $MONOLAKE_HOME/combined.pem /etc/nginx/ -sudo cp -r $MONOLAKE_HOME/benchmark/server/webroot/* /usr/share/nginx/html/ -sudo service nginx restart diff --git a/benchmark/server/webroot/server2/index.html b/benchmark/server/webroot/server2/index.html deleted file mode 100644 index c4b5785..0000000 --- a/benchmark/server/webroot/server2/index.html +++ /dev/null @@ -1,9 +0,0 @@ - - - - Tiny Size HTML - - -

Tiny Size HTML

- - \ No newline at end of file diff --git a/benchmark/server/webroot/server3/index.html b/benchmark/server/webroot/server3/index.html deleted file mode 100644 index cb3b4b8..0000000 --- a/benchmark/server/webroot/server3/index.html +++ /dev/null @@ -1,54 +0,0 @@ - - - - Small Size HTML - - -

Small Size HTML

-

- Silicon Valley is a region in the San Francisco Bay Area of California that is known for its high concentration of - technology companies, startups, and venture capital firms. It is home to some of the world's most innovative and - influential companies, including Apple, Google, Facebook, and Tesla, among many others. The region has a unique culture - that fosters entrepreneurship, innovation, and collaboration, which has helped to create a thriving ecosystem for tech - startups. -

- -

- The history of Silicon Valley can be traced back to the 1950s and 60s, when companies like Fairchild Semiconductor and - Intel were founded in the area. These early companies played a crucial role in the development of the microprocessor, - which is the basis for modern computer technology. As the demand for microprocessors grew, so did the number of - companies in the region that specialized in producing them. Today, Silicon Valley is home to thousands of tech - companies, including many startups that are working on cutting-edge technologies like artificial intelligence, robotics, - and renewable energy. -

- -

- One of the key factors that has contributed to the success of Silicon Valley is the presence of venture capital firms. - These firms provide funding for startups in exchange for equity, which helps to fuel their growth and development. Many - of the most successful tech companies in the world got their start in Silicon Valley, thanks in large part to the - support they received from local venture capital firms. -

- -

- Another important factor in Silicon Valley's success is the culture of innovation and collaboration that exists there. - The region has a strong tradition of entrepreneurship, and many companies are founded by individuals who have worked - together in the past or who share similar goals and values. This creates a sense of community and camaraderie that helps - to drive innovation and progress. -

- -

- Silicon Valley also has a number of other advantages that make it an attractive location for tech companies. The region - is home to some of the world's top universities, including Stanford University and the University of California, - Berkeley, which provide a steady supply of talented engineers and scientists. Additionally, Silicon Valley has a - well-developed infrastructure that supports the growth of tech companies, including high-speed internet access, reliable - transportation systems, and a diverse range of housing options. -

- -

- In conclusion, Silicon Valley is a unique and influential region in the world of technology. Its concentration of top - tech companies, venture capital firms, and innovative culture have helped to create a thriving ecosystem for startups - and established companies alike. As the tech industry continues to evolve and grow, it is likely that Silicon Valley - will remain at the forefront of innovation and progress. -

- - \ No newline at end of file diff --git a/benchmark/server/webroot/server4/index.html b/benchmark/server/webroot/server4/index.html deleted file mode 100644 index e32786e..0000000 --- a/benchmark/server/webroot/server4/index.html +++ /dev/null @@ -1,246 +0,0 @@ - - - - Medium Size HTML - - -

Medium Size HTML

-

- Large Language Models: Revolutionizing Natural Language Processing -

- -

- Introduction -

- -

- The field of natural language processing (NLP) has witnessed tremendous growth and advancements in recent years, with - the emergence of large language models being a significant contributor to this progress. These models have - revolutionized the way we approach NLP tasks, enabling us to perform complex linguistic operations with unprecedented - accuracy and efficiency. In this article, we will delve into the concept of large language models, their architectures, - applications, and the future prospects in this field. -

- -

- What are Large Language Models? -

- -

- Large language models are artificial intelligence (AI) models that are trained on vast amounts of text data to generate - language representations that can be used for various NLP tasks. These models are designed to learn the patterns and - structures of language, enabling them to generate coherent and contextually appropriate text. The key characteristic of - large language models is their scale, with some models containing billions of parameters and trillions of words in their - training datasets. -

- -

- Architectures of Large Language Models -

- -

- There are several types of large language models, each with its unique architecture and applications. Some of the most - popular architectures include: -

- -

- 1. Transformers: This architecture is based on a self-attention mechanism that allows the model to weigh the importance - of different words in a sequence. Transformers have achieved state-of-the-art results in various NLP tasks, including - language translation and text generation. -

-

- 2. Recurrent Neural Networks (RNNs): RNNs are designed to process sequences of data, making them well-suited for tasks - such as language modeling, machine translation, and text summarization. These models use loops to feed information from - one time step to the next, enabling them to capture temporal dependencies in language. -

-

- 3. Generative Adversarial Networks (GANs): GANs consist of two components: a generator network that generates text, and - a discriminator network that evaluates the generated text and provides feedback to the generator. This adversarial - process enables the generator to produce more realistic and coherent text over time. -

- -

- Applications of Large Language Models -

- -

- Large language models have numerous applications in NLP, including: -

- -

- 1. Language Translation: Large language models can be trained on parallel corpora of source and target languages to - learn the patterns and structures of both languages. This enables them to generate high-quality translations that are - contextually appropriate. -

-

- 2. Text Summarization: These models can be used to summarize long documents or articles, extracting the most important - information and generating a concise summary. -

-

- 3. Chatbots and Conversational Systems: Large language models can be used to generate responses to user queries in - chatbots and conversational systems, enabling them to understand and respond to complex questions and requests. -

-

- 4. Language Generation: These models can be used to generate coherent and contextually appropriate text, such as - articles, stories, or even entire books. -

- -

- Future Prospects of Large Language Models -

- -

- The future prospects of large language models are bright and promising. As these models continue to advance in scale and - sophistication, they will enable us to perform a wide range of NLP tasks with unprecedented accuracy and efficiency. - Some of the potential applications and advancements include: -

- -

- 1. Improved Language Translation: With the ability to learn multiple languages simultaneously, large language models - could potentially become multilingual, enabling them to translate between different language pairs more effectively. -

-

- 2. Enhanced Conversational Systems: As these models improve their ability to understand and respond to complex questions - and requests, they will become increasingly adept at engaging in natural-sounding conversations with humans. -

-

- 3. Increased Efficiency in NLP Tasks: Large language models have the potential to automate many NLP tasks, such as text - summarization, sentiment analysis, and question answering, freeing up human resources for more complex and creative - tasks. -

-

- 4. Advancements in Creative Writing: Large language models could potentially be used to generate original and coherent - creative writing, such as poetry, short stories, or even entire novels. -

- -

- Conclusion -

- -

- Large language models have revolutionized the field of NLP, enabling us to perform complex linguistic operations with - unprecedented accuracy and efficiency. These models have numerous applications in language translation, text - summarization, chatbots, and creative writing, among others. As these models continue to advance in scale and - sophistication, they will undoubtedly lead to further advancements in NLP and potentially even transform the way we - interact with technology. -

- -

- Large Language Models: Revolutionizing Natural Language Processing -

- -

- Introduction -

- -

- The field of natural language processing (NLP) has witnessed tremendous growth and advancements in recent years, with - the emergence of large language models being a significant contributor to this progress. These models have - revolutionized the way we approach NLP tasks, enabling us to perform complex linguistic operations with unprecedented - accuracy and efficiency. In this article, we will delve into the concept of large language models, their architectures, - applications, and the future prospects in this field. -

- -

- What are Large Language Models? -

- -

- Large language models are artificial intelligence (AI) models that are trained on vast amounts of text data to generate - language representations that can be used for various NLP tasks. These models are designed to learn the patterns and - structures of language, enabling them to generate coherent and contextually appropriate text. The key characteristic of - large language models is their scale, with some models containing billions of parameters and trillions of words in their - training datasets. -

- -

- Architectures of Large Language Models -

- -

- There are several types of large language models, each with its unique architecture and applications. Some of the most - popular architectures include: -

- -

- 1. Transformers: This architecture is based on a self-attention mechanism that allows the model to weigh the importance - of different words in a sequence. Transformers have achieved state-of-the-art results in various NLP tasks, including - language translation and text generation. -

-

- 2. Recurrent Neural Networks (RNNs): RNNs are designed to process sequences of data, making them well-suited for tasks - such as language modeling, machine translation, and text summarization. These models use loops to feed information from - one time step to the next, enabling them to capture temporal dependencies in language. -

-

- 3. Generative Adversarial Networks (GANs): GANs consist of two components: a generator network that generates text, and - a discriminator network that evaluates the generated text and provides feedback to the generator. This adversarial - process enables the generator to produce more realistic and coherent text over time. -

- -

- Applications of Large Language Models -

- -

- Large language models have numerous applications in NLP, including: -

- -

- 1. Language Translation: Large language models can be trained on parallel corpora of source and target languages to - learn the patterns and structures of both languages. This enables them to generate high-quality translations that are - contextually appropriate. -

-

- 2. Text Summarization: These models can be used to summarize long documents or articles, extracting the most important - information and generating a concise summary. -

-

- 3. Chatbots and Conversational Systems: Large language models can be used to generate responses to user queries in - chatbots and conversational systems, enabling them to understand and respond to complex questions and requests. -

-

- 4. Language Generation: These models can be used to generate coherent and contextually appropriate text, such as - articles, stories, or even entire books. -

- -

- Future Prospects of Large Language Models -

- -

- The future prospects of large language models are bright and promising. As these models continue to advance in scale and - sophistication, they will enable us to perform a wide range of NLP tasks with unprecedented accuracy and efficiency. - Some of the potential applications and advancements include: -

- -

- 1. Improved Language Translation: With the ability to learn multiple languages simultaneously, large language models - could potentially become multilingual, enabling them to translate between different language pairs more effectively. -

-

- 2. Enhanced Conversational Systems: As these models improve their ability to understand and respond to complex questions - and requests, they will become increasingly adept at engaging in natural-sounding conversations with humans. -

-

- 3. Increased Efficiency in NLP Tasks: Large language models have the potential to automate many NLP tasks, such as text - summarization, sentiment analysis, and question answering, freeing up human resources for more complex and creative - tasks. -

-

- 4. Advancements in Creative Writing: Large language models could potentially be used to generate original and coherent - creative writing, such as poetry, short stories, or even entire novels. -

- -

- Conclusion -

- -

- Large language models have revolutionized the field of NLP, enabling us to perform complex linguistic operations with - unprecedented accuracy and efficiency. These models have numerous applications in language translation, text - summarization, chatbots, and creative writing, among others. As these models continue to advance in scale and - sophistication, they will undoubtedly lead to further advancements in NLP and potentially even transform the way we - interact with technology. -

- - diff --git a/benchmark/server/webroot/server5/index.html b/benchmark/server/webroot/server5/index.html deleted file mode 100644 index df8958d..0000000 --- a/benchmark/server/webroot/server5/index.html +++ /dev/null @@ -1,3464 +0,0 @@ - - - - Large Size HTML - - -

Large Size HTML

- -

- Large Language Models: Revolutionizing Natural Language Processing -

- -

- Introduction -

- -

- The field of natural language processing (NLP) has witnessed tremendous growth and advancements in recent years, with - the emergence of large language models being a significant contributor to this progress. These models have - revolutionized the way we approach NLP tasks, enabling us to perform complex linguistic operations with unprecedented - accuracy and efficiency. In this article, we will delve into the concept of large language models, their architectures, - applications, and the future prospects in this field. -

- -

- What are Large Language Models? -

- -

- Large language models are artificial intelligence (AI) models that are trained on vast amounts of text data to generate - language representations that can be used for various NLP tasks. These models are designed to learn the patterns and - structures of language, enabling them to generate coherent and contextually appropriate text. The key characteristic of - large language models is their scale, with some models containing billions of parameters and trillions of words in their - training datasets. -

- -

- Architectures of Large Language Models -

- -

- There are several types of large language models, each with its unique architecture and applications. Some of the most - popular architectures include: -

- -

- 1. Transformers: This architecture is based on a self-attention mechanism that allows the model to weigh the importance - of different words in a sequence. Transformers have achieved state-of-the-art results in various NLP tasks, including - language translation and text generation. -

-

- 2. Recurrent Neural Networks (RNNs): RNNs are designed to process sequences of data, making them well-suited for tasks - such as language modeling, machine translation, and text summarization. These models use loops to feed information from - one time step to the next, enabling them to capture temporal dependencies in language. -

-

- 3. Generative Adversarial Networks (GANs): GANs consist of two components: a generator network that generates text, and - a discriminator network that evaluates the generated text and provides feedback to the generator. This adversarial - process enables the generator to produce more realistic and coherent text over time. -

- -

- Applications of Large Language Models -

- -

- Large language models have numerous applications in NLP, including: -

- -

- 1. Language Translation: Large language models can be trained on parallel corpora of source and target languages to - learn the patterns and structures of both languages. This enables them to generate high-quality translations that are - contextually appropriate. -

-

- 2. Text Summarization: These models can be used to summarize long documents or articles, extracting the most important - information and generating a concise summary. -

-

- 3. Chatbots and Conversational Systems: Large language models can be used to generate responses to user queries in - chatbots and conversational systems, enabling them to understand and respond to complex questions and requests. -

-

- 4. Language Generation: These models can be used to generate coherent and contextually appropriate text, such as - articles, stories, or even entire books. -

- -

- Future Prospects of Large Language Models -

- -

- The future prospects of large language models are bright and promising. As these models continue to advance in scale and - sophistication, they will enable us to perform a wide range of NLP tasks with unprecedented accuracy and efficiency. - Some of the potential applications and advancements include: -

- -

- 1. Improved Language Translation: With the ability to learn multiple languages simultaneously, large language models - could potentially become multilingual, enabling them to translate between different language pairs more effectively. -

-

- 2. Enhanced Conversational Systems: As these models improve their ability to understand and respond to complex questions - and requests, they will become increasingly adept at engaging in natural-sounding conversations with humans. -

-

- 3. Increased Efficiency in NLP Tasks: Large language models have the potential to automate many NLP tasks, such as text - summarization, sentiment analysis, and question answering, freeing up human resources for more complex and creative - tasks. -

-

- 4. Advancements in Creative Writing: Large language models could potentially be used to generate original and coherent - creative writing, such as poetry, short stories, or even entire novels. -

- -

- Conclusion -

- -

- Large language models have revolutionized the field of NLP, enabling us to perform complex linguistic operations with - unprecedented accuracy and efficiency. These models have numerous applications in language translation, text - summarization, chatbots, and creative writing, among others. As these models continue to advance in scale and - sophistication, they will undoubtedly lead to further advancements in NLP and potentially even transform the way we - interact with technology. -

- -

- Introduction -

- -

- The field of natural language processing (NLP) has witnessed tremendous growth and advancements in recent years, with - the emergence of large language models being a significant contributor to this progress. These models have - revolutionized the way we approach NLP tasks, enabling us to perform complex linguistic operations with unprecedented - accuracy and efficiency. In this article, we will delve into the concept of large language models, their architectures, - applications, and the future prospects in this field. -

- -

- What are Large Language Models? -

- -

- Large language models are artificial intelligence (AI) models that are trained on vast amounts of text data to generate - language representations that can be used for various NLP tasks. These models are designed to learn the patterns and - structures of language, enabling them to generate coherent and contextually appropriate text. The key characteristic of - large language models is their scale, with some models containing billions of parameters and trillions of words in their - training datasets. -

- -

- Architectures of Large Language Models -

- -

- There are several types of large language models, each with its unique architecture and applications. Some of the most - popular architectures include: -

- -

- 1. Transformers: This architecture is based on a self-attention mechanism that allows the model to weigh the importance - of different words in a sequence. Transformers have achieved state-of-the-art results in various NLP tasks, including - language translation and text generation. -

-

- 2. Recurrent Neural Networks (RNNs): RNNs are designed to process sequences of data, making them well-suited for tasks - such as language modeling, machine translation, and text summarization. These models use loops to feed information from - one time step to the next, enabling them to capture temporal dependencies in language. -

-

- 3. Generative Adversarial Networks (GANs): GANs consist of two components: a generator network that generates text, and - a discriminator network that evaluates the generated text and provides feedback to the generator. This adversarial - process enables the generator to produce more realistic and coherent text over time. -

- -

- Applications of Large Language Models -

- -

- Large language models have numerous applications in NLP, including: -

- -

- 1. Language Translation: Large language models can be trained on parallel corpora of source and target languages to - learn the patterns and structures of both languages. This enables them to generate high-quality translations that are - contextually appropriate. -

-

- 2. Text Summarization: These models can be used to summarize long documents or articles, extracting the most important - information and generating a concise summary. -

-

- 3. Chatbots and Conversational Systems: Large language models can be used to generate responses to user queries in - chatbots and conversational systems, enabling them to understand and respond to complex questions and requests. -

-

- 4. Language Generation: These models can be used to generate coherent and contextually appropriate text, such as - articles, stories, or even entire books. -

- -

- Future Prospects of Large Language Models -

- -

- The future prospects of large language models are bright and promising. As these models continue to advance in scale and - sophistication, they will enable us to perform a wide range of NLP tasks with unprecedented accuracy and efficiency. - Some of the potential applications and advancements include: -

- -

- 1. Improved Language Translation: With the ability to learn multiple languages simultaneously, large language models - could potentially become multilingual, enabling them to translate between different language pairs more effectively. -

-

- 2. Enhanced Conversational Systems: As these models improve their ability to understand and respond to complex questions - and requests, they will become increasingly adept at engaging in natural-sounding conversations with humans. -

-

- 3. Increased Efficiency in NLP Tasks: Large language models have the potential to automate many NLP tasks, such as text - summarization, sentiment analysis, and question answering, freeing up human resources for more complex and creative - tasks. -

-

- 4. Advancements in Creative Writing: Large language models could potentially be used to generate original and coherent - creative writing, such as poetry, short stories, or even entire novels. -

- -

- Conclusion -

- -

- Large language models have revolutionized the field of NLP, enabling us to perform complex linguistic operations with - unprecedented accuracy and efficiency. These models have numerous applications in language translation, text - summarization, chatbots, and creative writing, among others. As these models continue to advance in scale and - sophistication, they will undoubtedly lead to further advancements in NLP and potentially even transform the way we - interact with technology. -

- -

- Introduction -

- -

- The field of natural language processing (NLP) has witnessed tremendous growth and advancements in recent years, with - the emergence of large language models being a significant contributor to this progress. These models have - revolutionized the way we approach NLP tasks, enabling us to perform complex linguistic operations with unprecedented - accuracy and efficiency. In this article, we will delve into the concept of large language models, their architectures, - applications, and the future prospects in this field. -

- -

- What are Large Language Models? -

- -

- Large language models are artificial intelligence (AI) models that are trained on vast amounts of text data to generate - language representations that can be used for various NLP tasks. These models are designed to learn the patterns and - structures of language, enabling them to generate coherent and contextually appropriate text. The key characteristic of - large language models is their scale, with some models containing billions of parameters and trillions of words in their - training datasets. -

- -

- Architectures of Large Language Models -

- -

- There are several types of large language models, each with its unique architecture and applications. Some of the most - popular architectures include: -

- -

- 1. Transformers: This architecture is based on a self-attention mechanism that allows the model to weigh the importance - of different words in a sequence. Transformers have achieved state-of-the-art results in various NLP tasks, including - language translation and text generation. -

-

- 2. Recurrent Neural Networks (RNNs): RNNs are designed to process sequences of data, making them well-suited for tasks - such as language modeling, machine translation, and text summarization. These models use loops to feed information from - one time step to the next, enabling them to capture temporal dependencies in language. -

-

- 3. Generative Adversarial Networks (GANs): GANs consist of two components: a generator network that generates text, and - a discriminator network that evaluates the generated text and provides feedback to the generator. This adversarial - process enables the generator to produce more realistic and coherent text over time. -

- -

- Applications of Large Language Models -

- -

- Large language models have numerous applications in NLP, including: -

- -

- 1. Language Translation: Large language models can be trained on parallel corpora of source and target languages to - learn the patterns and structures of both languages. This enables them to generate high-quality translations that are - contextually appropriate. -

-

- 2. Text Summarization: These models can be used to summarize long documents or articles, extracting the most important - information and generating a concise summary. -

-

- 3. Chatbots and Conversational Systems: Large language models can be used to generate responses to user queries in - chatbots and conversational systems, enabling them to understand and respond to complex questions and requests. -

-

- 4. Language Generation: These models can be used to generate coherent and contextually appropriate text, such as - articles, stories, or even entire books. -

- -

- Future Prospects of Large Language Models -

- -

- The future prospects of large language models are bright and promising. As these models continue to advance in scale and - sophistication, they will enable us to perform a wide range of NLP tasks with unprecedented accuracy and efficiency. - Some of the potential applications and advancements include: -

- -

- 1. Improved Language Translation: With the ability to learn multiple languages simultaneously, large language models - could potentially become multilingual, enabling them to translate between different language pairs more effectively. -

-

- 2. Enhanced Conversational Systems: As these models improve their ability to understand and respond to complex questions - and requests, they will become increasingly adept at engaging in natural-sounding conversations with humans. -

-

- 3. Increased Efficiency in NLP Tasks: Large language models have the potential to automate many NLP tasks, such as text - summarization, sentiment analysis, and question answering, freeing up human resources for more complex and creative - tasks. -

-

- 4. Advancements in Creative Writing: Large language models could potentially be used to generate original and coherent - creative writing, such as poetry, short stories, or even entire novels. -

- -

- Conclusion -

- -

- Large language models have revolutionized the field of NLP, enabling us to perform complex linguistic operations with - unprecedented accuracy and efficiency. These models have numerous applications in language translation, text - summarization, chatbots, and creative writing, among others. As these models continue to advance in scale and - sophistication, they will undoubtedly lead to further advancements in NLP and potentially even transform the way we - interact with technology. -

- -

- Introduction -

- -

- The field of natural language processing (NLP) has witnessed tremendous growth and advancements in recent years, with - the emergence of large language models being a significant contributor to this progress. These models have - revolutionized the way we approach NLP tasks, enabling us to perform complex linguistic operations with unprecedented - accuracy and efficiency. In this article, we will delve into the concept of large language models, their architectures, - applications, and the future prospects in this field. -

- -

- What are Large Language Models? -

- -

- Large language models are artificial intelligence (AI) models that are trained on vast amounts of text data to generate - language representations that can be used for various NLP tasks. These models are designed to learn the patterns and - structures of language, enabling them to generate coherent and contextually appropriate text. The key characteristic of - large language models is their scale, with some models containing billions of parameters and trillions of words in their - training datasets. -

- -

- Architectures of Large Language Models -

- -

- There are several types of large language models, each with its unique architecture and applications. Some of the most - popular architectures include: -

- -

- 1. Transformers: This architecture is based on a self-attention mechanism that allows the model to weigh the importance - of different words in a sequence. Transformers have achieved state-of-the-art results in various NLP tasks, including - language translation and text generation. -

-

- 2. Recurrent Neural Networks (RNNs): RNNs are designed to process sequences of data, making them well-suited for tasks - such as language modeling, machine translation, and text summarization. These models use loops to feed information from - one time step to the next, enabling them to capture temporal dependencies in language. -

-

- 3. Generative Adversarial Networks (GANs): GANs consist of two components: a generator network that generates text, and - a discriminator network that evaluates the generated text and provides feedback to the generator. This adversarial - process enables the generator to produce more realistic and coherent text over time. -

- -

- Applications of Large Language Models -

- -

- Large language models have numerous applications in NLP, including: -

- -

- 1. Language Translation: Large language models can be trained on parallel corpora of source and target languages to - learn the patterns and structures of both languages. This enables them to generate high-quality translations that are - contextually appropriate. -

-

- 2. Text Summarization: These models can be used to summarize long documents or articles, extracting the most important - information and generating a concise summary. -

-

- 3. Chatbots and Conversational Systems: Large language models can be used to generate responses to user queries in - chatbots and conversational systems, enabling them to understand and respond to complex questions and requests. -

-

- 4. Language Generation: These models can be used to generate coherent and contextually appropriate text, such as - articles, stories, or even entire books. -

- -

- Future Prospects of Large Language Models -

- -

- The future prospects of large language models are bright and promising. As these models continue to advance in scale and - sophistication, they will enable us to perform a wide range of NLP tasks with unprecedented accuracy and efficiency. - Some of the potential applications and advancements include: -

- -

- 1. Improved Language Translation: With the ability to learn multiple languages simultaneously, large language models - could potentially become multilingual, enabling them to translate between different language pairs more effectively. -

-

- 2. Enhanced Conversational Systems: As these models improve their ability to understand and respond to complex questions - and requests, they will become increasingly adept at engaging in natural-sounding conversations with humans. -

-

- 3. Increased Efficiency in NLP Tasks: Large language models have the potential to automate many NLP tasks, such as text - summarization, sentiment analysis, and question answering, freeing up human resources for more complex and creative - tasks. -

-

- 4. Advancements in Creative Writing: Large language models could potentially be used to generate original and coherent - creative writing, such as poetry, short stories, or even entire novels. -

- -

- Conclusion -

- -

- Large language models have revolutionized the field of NLP, enabling us to perform complex linguistic operations with - unprecedented accuracy and efficiency. These models have numerous applications in language translation, text - summarization, chatbots, and creative writing, among others. As these models continue to advance in scale and - sophistication, they will undoubtedly lead to further advancements in NLP and potentially even transform the way we - interact with technology. -

- -

- Introduction -

- -

- The field of natural language processing (NLP) has witnessed tremendous growth and advancements in recent years, with - the emergence of large language models being a significant contributor to this progress. These models have - revolutionized the way we approach NLP tasks, enabling us to perform complex linguistic operations with unprecedented - accuracy and efficiency. In this article, we will delve into the concept of large language models, their architectures, - applications, and the future prospects in this field. -

- -

- What are Large Language Models? -

- -

- Large language models are artificial intelligence (AI) models that are trained on vast amounts of text data to generate - language representations that can be used for various NLP tasks. These models are designed to learn the patterns and - structures of language, enabling them to generate coherent and contextually appropriate text. The key characteristic of - large language models is their scale, with some models containing billions of parameters and trillions of words in their - training datasets. -

- -

- Architectures of Large Language Models -

- -

- There are several types of large language models, each with its unique architecture and applications. Some of the most - popular architectures include: -

- -

- 1. Transformers: This architecture is based on a self-attention mechanism that allows the model to weigh the importance - of different words in a sequence. Transformers have achieved state-of-the-art results in various NLP tasks, including - language translation and text generation. -

-

- 2. Recurrent Neural Networks (RNNs): RNNs are designed to process sequences of data, making them well-suited for tasks - such as language modeling, machine translation, and text summarization. These models use loops to feed information from - one time step to the next, enabling them to capture temporal dependencies in language. -

-

- 3. Generative Adversarial Networks (GANs): GANs consist of two components: a generator network that generates text, and - a discriminator network that evaluates the generated text and provides feedback to the generator. This adversarial - process enables the generator to produce more realistic and coherent text over time. -

- -

- Applications of Large Language Models -

- -

- Large language models have numerous applications in NLP, including: -

- -

- 1. Language Translation: Large language models can be trained on parallel corpora of source and target languages to - learn the patterns and structures of both languages. This enables them to generate high-quality translations that are - contextually appropriate. -

-

- 2. Text Summarization: These models can be used to summarize long documents or articles, extracting the most important - information and generating a concise summary. -

-

- 3. Chatbots and Conversational Systems: Large language models can be used to generate responses to user queries in - chatbots and conversational systems, enabling them to understand and respond to complex questions and requests. -

-

- 4. Language Generation: These models can be used to generate coherent and contextually appropriate text, such as - articles, stories, or even entire books. -

- -

- Future Prospects of Large Language Models -

- -

- The future prospects of large language models are bright and promising. As these models continue to advance in scale and - sophistication, they will enable us to perform a wide range of NLP tasks with unprecedented accuracy and efficiency. - Some of the potential applications and advancements include: -

- -

- 1. Improved Language Translation: With the ability to learn multiple languages simultaneously, large language models - could potentially become multilingual, enabling them to translate between different language pairs more effectively. -

-

- 2. Enhanced Conversational Systems: As these models improve their ability to understand and respond to complex questions - and requests, they will become increasingly adept at engaging in natural-sounding conversations with humans. -

-

- 3. Increased Efficiency in NLP Tasks: Large language models have the potential to automate many NLP tasks, such as text - summarization, sentiment analysis, and question answering, freeing up human resources for more complex and creative - tasks. -

-

- 4. Advancements in Creative Writing: Large language models could potentially be used to generate original and coherent - creative writing, such as poetry, short stories, or even entire novels. -

- -

- Conclusion -

- -

- Large language models have revolutionized the field of NLP, enabling us to perform complex linguistic operations with - unprecedented accuracy and efficiency. These models have numerous applications in language translation, text - summarization, chatbots, and creative writing, among others. As these models continue to advance in scale and - sophistication, they will undoubtedly lead to further advancements in NLP and potentially even transform the way we - interact with technology. -

- -

- Introduction -

- -

- The field of natural language processing (NLP) has witnessed tremendous growth and advancements in recent years, with - the emergence of large language models being a significant contributor to this progress. These models have - revolutionized the way we approach NLP tasks, enabling us to perform complex linguistic operations with unprecedented - accuracy and efficiency. In this article, we will delve into the concept of large language models, their architectures, - applications, and the future prospects in this field. -

- -

- What are Large Language Models? -

- -

- Large language models are artificial intelligence (AI) models that are trained on vast amounts of text data to generate - language representations that can be used for various NLP tasks. These models are designed to learn the patterns and - structures of language, enabling them to generate coherent and contextually appropriate text. The key characteristic of - large language models is their scale, with some models containing billions of parameters and trillions of words in their - training datasets. -

- -

- Architectures of Large Language Models -

- -

- There are several types of large language models, each with its unique architecture and applications. Some of the most - popular architectures include: -

- -

- 1. Transformers: This architecture is based on a self-attention mechanism that allows the model to weigh the importance - of different words in a sequence. Transformers have achieved state-of-the-art results in various NLP tasks, including - language translation and text generation. -

-

- 2. Recurrent Neural Networks (RNNs): RNNs are designed to process sequences of data, making them well-suited for tasks - such as language modeling, machine translation, and text summarization. These models use loops to feed information from - one time step to the next, enabling them to capture temporal dependencies in language. -

-

- 3. Generative Adversarial Networks (GANs): GANs consist of two components: a generator network that generates text, and - a discriminator network that evaluates the generated text and provides feedback to the generator. This adversarial - process enables the generator to produce more realistic and coherent text over time. -

- -

- Applications of Large Language Models -

- -

- Large language models have numerous applications in NLP, including: -

- -

- 1. Language Translation: Large language models can be trained on parallel corpora of source and target languages to - learn the patterns and structures of both languages. This enables them to generate high-quality translations that are - contextually appropriate. -

-

- 2. Text Summarization: These models can be used to summarize long documents or articles, extracting the most important - information and generating a concise summary. -

-

- 3. Chatbots and Conversational Systems: Large language models can be used to generate responses to user queries in - chatbots and conversational systems, enabling them to understand and respond to complex questions and requests. -

-

- 4. Language Generation: These models can be used to generate coherent and contextually appropriate text, such as - articles, stories, or even entire books. -

- -

- Future Prospects of Large Language Models -

- -

- The future prospects of large language models are bright and promising. As these models continue to advance in scale and - sophistication, they will enable us to perform a wide range of NLP tasks with unprecedented accuracy and efficiency. - Some of the potential applications and advancements include: -

- -

- 1. Improved Language Translation: With the ability to learn multiple languages simultaneously, large language models - could potentially become multilingual, enabling them to translate between different language pairs more effectively. -

-

- 2. Enhanced Conversational Systems: As these models improve their ability to understand and respond to complex questions - and requests, they will become increasingly adept at engaging in natural-sounding conversations with humans. -

-

- 3. Increased Efficiency in NLP Tasks: Large language models have the potential to automate many NLP tasks, such as text - summarization, sentiment analysis, and question answering, freeing up human resources for more complex and creative - tasks. -

-

- 4. Advancements in Creative Writing: Large language models could potentially be used to generate original and coherent - creative writing, such as poetry, short stories, or even entire novels. -

- -

- Conclusion -

- -

- Large language models have revolutionized the field of NLP, enabling us to perform complex linguistic operations with - unprecedented accuracy and efficiency. These models have numerous applications in language translation, text - summarization, chatbots, and creative writing, among others. As these models continue to advance in scale and - sophistication, they will undoubtedly lead to further advancements in NLP and potentially even transform the way we - interact with technology. -

- -

- Introduction -

- -

- The field of natural language processing (NLP) has witnessed tremendous growth and advancements in recent years, with - the emergence of large language models being a significant contributor to this progress. These models have - revolutionized the way we approach NLP tasks, enabling us to perform complex linguistic operations with unprecedented - accuracy and efficiency. In this article, we will delve into the concept of large language models, their architectures, - applications, and the future prospects in this field. -

- -

- What are Large Language Models? -

- -

- Large language models are artificial intelligence (AI) models that are trained on vast amounts of text data to generate - language representations that can be used for various NLP tasks. These models are designed to learn the patterns and - structures of language, enabling them to generate coherent and contextually appropriate text. The key characteristic of - large language models is their scale, with some models containing billions of parameters and trillions of words in their - training datasets. -

- -

- Architectures of Large Language Models -

- -

- There are several types of large language models, each with its unique architecture and applications. Some of the most - popular architectures include: -

- -

- 1. Transformers: This architecture is based on a self-attention mechanism that allows the model to weigh the importance - of different words in a sequence. Transformers have achieved state-of-the-art results in various NLP tasks, including - language translation and text generation. -

-

- 2. Recurrent Neural Networks (RNNs): RNNs are designed to process sequences of data, making them well-suited for tasks - such as language modeling, machine translation, and text summarization. These models use loops to feed information from - one time step to the next, enabling them to capture temporal dependencies in language. -

-

- 3. Generative Adversarial Networks (GANs): GANs consist of two components: a generator network that generates text, and - a discriminator network that evaluates the generated text and provides feedback to the generator. This adversarial - process enables the generator to produce more realistic and coherent text over time. -

- -

- Applications of Large Language Models -

- -

- Large language models have numerous applications in NLP, including: -

- -

- 1. Language Translation: Large language models can be trained on parallel corpora of source and target languages to - learn the patterns and structures of both languages. This enables them to generate high-quality translations that are - contextually appropriate. -

-

- 2. Text Summarization: These models can be used to summarize long documents or articles, extracting the most important - information and generating a concise summary. -

-

- 3. Chatbots and Conversational Systems: Large language models can be used to generate responses to user queries in - chatbots and conversational systems, enabling them to understand and respond to complex questions and requests. -

-

- 4. Language Generation: These models can be used to generate coherent and contextually appropriate text, such as - articles, stories, or even entire books. -

- -

- Future Prospects of Large Language Models -

- -

- The future prospects of large language models are bright and promising. As these models continue to advance in scale and - sophistication, they will enable us to perform a wide range of NLP tasks with unprecedented accuracy and efficiency. - Some of the potential applications and advancements include: -

- -

- 1. Improved Language Translation: With the ability to learn multiple languages simultaneously, large language models - could potentially become multilingual, enabling them to translate between different language pairs more effectively. -

-

- 2. Enhanced Conversational Systems: As these models improve their ability to understand and respond to complex questions - and requests, they will become increasingly adept at engaging in natural-sounding conversations with humans. -

-

- 3. Increased Efficiency in NLP Tasks: Large language models have the potential to automate many NLP tasks, such as text - summarization, sentiment analysis, and question answering, freeing up human resources for more complex and creative - tasks. -

-

- 4. Advancements in Creative Writing: Large language models could potentially be used to generate original and coherent - creative writing, such as poetry, short stories, or even entire novels. -

- -

- Conclusion -

- -

- Large language models have revolutionized the field of NLP, enabling us to perform complex linguistic operations with - unprecedented accuracy and efficiency. These models have numerous applications in language translation, text - summarization, chatbots, and creative writing, among others. As these models continue to advance in scale and - sophistication, they will undoubtedly lead to further advancements in NLP and potentially even transform the way we - interact with technology. -

- -

- Introduction -

- -

- The field of natural language processing (NLP) has witnessed tremendous growth and advancements in recent years, with - the emergence of large language models being a significant contributor to this progress. These models have - revolutionized the way we approach NLP tasks, enabling us to perform complex linguistic operations with unprecedented - accuracy and efficiency. In this article, we will delve into the concept of large language models, their architectures, - applications, and the future prospects in this field. -

- -

- What are Large Language Models? -

- -

- Large language models are artificial intelligence (AI) models that are trained on vast amounts of text data to generate - language representations that can be used for various NLP tasks. These models are designed to learn the patterns and - structures of language, enabling them to generate coherent and contextually appropriate text. The key characteristic of - large language models is their scale, with some models containing billions of parameters and trillions of words in their - training datasets. -

- -

- Architectures of Large Language Models -

- -

- There are several types of large language models, each with its unique architecture and applications. Some of the most - popular architectures include: -

- -

- 1. Transformers: This architecture is based on a self-attention mechanism that allows the model to weigh the importance - of different words in a sequence. Transformers have achieved state-of-the-art results in various NLP tasks, including - language translation and text generation. -

-

- 2. Recurrent Neural Networks (RNNs): RNNs are designed to process sequences of data, making them well-suited for tasks - such as language modeling, machine translation, and text summarization. These models use loops to feed information from - one time step to the next, enabling them to capture temporal dependencies in language. -

-

- 3. Generative Adversarial Networks (GANs): GANs consist of two components: a generator network that generates text, and - a discriminator network that evaluates the generated text and provides feedback to the generator. This adversarial - process enables the generator to produce more realistic and coherent text over time. -

- -

- Applications of Large Language Models -

- -

- Large language models have numerous applications in NLP, including: -

- -

- 1. Language Translation: Large language models can be trained on parallel corpora of source and target languages to - learn the patterns and structures of both languages. This enables them to generate high-quality translations that are - contextually appropriate. -

-

- 2. Text Summarization: These models can be used to summarize long documents or articles, extracting the most important - information and generating a concise summary. -

-

- 3. Chatbots and Conversational Systems: Large language models can be used to generate responses to user queries in - chatbots and conversational systems, enabling them to understand and respond to complex questions and requests. -

-

- 4. Language Generation: These models can be used to generate coherent and contextually appropriate text, such as - articles, stories, or even entire books. -

- -

- Future Prospects of Large Language Models -

- -

- The future prospects of large language models are bright and promising. As these models continue to advance in scale and - sophistication, they will enable us to perform a wide range of NLP tasks with unprecedented accuracy and efficiency. - Some of the potential applications and advancements include: -

- -

- 1. Improved Language Translation: With the ability to learn multiple languages simultaneously, large language models - could potentially become multilingual, enabling them to translate between different language pairs more effectively. -

-

- 2. Enhanced Conversational Systems: As these models improve their ability to understand and respond to complex questions - and requests, they will become increasingly adept at engaging in natural-sounding conversations with humans. -

-

- 3. Increased Efficiency in NLP Tasks: Large language models have the potential to automate many NLP tasks, such as text - summarization, sentiment analysis, and question answering, freeing up human resources for more complex and creative - tasks. -

-

- 4. Advancements in Creative Writing: Large language models could potentially be used to generate original and coherent - creative writing, such as poetry, short stories, or even entire novels. -

- -

- Conclusion -

- -

- Large language models have revolutionized the field of NLP, enabling us to perform complex linguistic operations with - unprecedented accuracy and efficiency. These models have numerous applications in language translation, text - summarization, chatbots, and creative writing, among others. As these models continue to advance in scale and - sophistication, they will undoubtedly lead to further advancements in NLP and potentially even transform the way we - interact with technology. -

- -

- Introduction -

- -

- The field of natural language processing (NLP) has witnessed tremendous growth and advancements in recent years, with - the emergence of large language models being a significant contributor to this progress. These models have - revolutionized the way we approach NLP tasks, enabling us to perform complex linguistic operations with unprecedented - accuracy and efficiency. In this article, we will delve into the concept of large language models, their architectures, - applications, and the future prospects in this field. -

- -

- What are Large Language Models? -

- -

- Large language models are artificial intelligence (AI) models that are trained on vast amounts of text data to generate - language representations that can be used for various NLP tasks. These models are designed to learn the patterns and - structures of language, enabling them to generate coherent and contextually appropriate text. The key characteristic of - large language models is their scale, with some models containing billions of parameters and trillions of words in their - training datasets. -

- -

- Architectures of Large Language Models -

- -

- There are several types of large language models, each with its unique architecture and applications. Some of the most - popular architectures include: -

- -

- 1. Transformers: This architecture is based on a self-attention mechanism that allows the model to weigh the importance - of different words in a sequence. Transformers have achieved state-of-the-art results in various NLP tasks, including - language translation and text generation. -

-

- 2. Recurrent Neural Networks (RNNs): RNNs are designed to process sequences of data, making them well-suited for tasks - such as language modeling, machine translation, and text summarization. These models use loops to feed information from - one time step to the next, enabling them to capture temporal dependencies in language. -

-

- 3. Generative Adversarial Networks (GANs): GANs consist of two components: a generator network that generates text, and - a discriminator network that evaluates the generated text and provides feedback to the generator. This adversarial - process enables the generator to produce more realistic and coherent text over time. -

- -

- Applications of Large Language Models -

- -

- Large language models have numerous applications in NLP, including: -

- -

- 1. Language Translation: Large language models can be trained on parallel corpora of source and target languages to - learn the patterns and structures of both languages. This enables them to generate high-quality translations that are - contextually appropriate. -

-

- 2. Text Summarization: These models can be used to summarize long documents or articles, extracting the most important - information and generating a concise summary. -

-

- 3. Chatbots and Conversational Systems: Large language models can be used to generate responses to user queries in - chatbots and conversational systems, enabling them to understand and respond to complex questions and requests. -

-

- 4. Language Generation: These models can be used to generate coherent and contextually appropriate text, such as - articles, stories, or even entire books. -

- -

- Future Prospects of Large Language Models -

- -

- The future prospects of large language models are bright and promising. As these models continue to advance in scale and - sophistication, they will enable us to perform a wide range of NLP tasks with unprecedented accuracy and efficiency. - Some of the potential applications and advancements include: -

- -

- 1. Improved Language Translation: With the ability to learn multiple languages simultaneously, large language models - could potentially become multilingual, enabling them to translate between different language pairs more effectively. -

-

- 2. Enhanced Conversational Systems: As these models improve their ability to understand and respond to complex questions - and requests, they will become increasingly adept at engaging in natural-sounding conversations with humans. -

-

- 3. Increased Efficiency in NLP Tasks: Large language models have the potential to automate many NLP tasks, such as text - summarization, sentiment analysis, and question answering, freeing up human resources for more complex and creative - tasks. -

-

- 4. Advancements in Creative Writing: Large language models could potentially be used to generate original and coherent - creative writing, such as poetry, short stories, or even entire novels. -

- -

- Conclusion -

- -

- Large language models have revolutionized the field of NLP, enabling us to perform complex linguistic operations with - unprecedented accuracy and efficiency. These models have numerous applications in language translation, text - summarization, chatbots, and creative writing, among others. As these models continue to advance in scale and - sophistication, they will undoubtedly lead to further advancements in NLP and potentially even transform the way we - interact with technology. -

- -

- Introduction -

- -

- The field of natural language processing (NLP) has witnessed tremendous growth and advancements in recent years, with - the emergence of large language models being a significant contributor to this progress. These models have - revolutionized the way we approach NLP tasks, enabling us to perform complex linguistic operations with unprecedented - accuracy and efficiency. In this article, we will delve into the concept of large language models, their architectures, - applications, and the future prospects in this field. -

- -

- What are Large Language Models? -

- -

- Large language models are artificial intelligence (AI) models that are trained on vast amounts of text data to generate - language representations that can be used for various NLP tasks. These models are designed to learn the patterns and - structures of language, enabling them to generate coherent and contextually appropriate text. The key characteristic of - large language models is their scale, with some models containing billions of parameters and trillions of words in their - training datasets. -

- -

- Architectures of Large Language Models -

- -

- There are several types of large language models, each with its unique architecture and applications. Some of the most - popular architectures include: -

- -

- 1. Transformers: This architecture is based on a self-attention mechanism that allows the model to weigh the importance - of different words in a sequence. Transformers have achieved state-of-the-art results in various NLP tasks, including - language translation and text generation. -

-

- 2. Recurrent Neural Networks (RNNs): RNNs are designed to process sequences of data, making them well-suited for tasks - such as language modeling, machine translation, and text summarization. These models use loops to feed information from - one time step to the next, enabling them to capture temporal dependencies in language. -

-

- 3. Generative Adversarial Networks (GANs): GANs consist of two components: a generator network that generates text, and - a discriminator network that evaluates the generated text and provides feedback to the generator. This adversarial - process enables the generator to produce more realistic and coherent text over time. -

- -

- Applications of Large Language Models -

- -

- Large language models have numerous applications in NLP, including: -

- -

- 1. Language Translation: Large language models can be trained on parallel corpora of source and target languages to - learn the patterns and structures of both languages. This enables them to generate high-quality translations that are - contextually appropriate. -

-

- 2. Text Summarization: These models can be used to summarize long documents or articles, extracting the most important - information and generating a concise summary. -

-

- 3. Chatbots and Conversational Systems: Large language models can be used to generate responses to user queries in - chatbots and conversational systems, enabling them to understand and respond to complex questions and requests. -

-

- 4. Language Generation: These models can be used to generate coherent and contextually appropriate text, such as - articles, stories, or even entire books. -

- -

- Future Prospects of Large Language Models -

- -

- The future prospects of large language models are bright and promising. As these models continue to advance in scale and - sophistication, they will enable us to perform a wide range of NLP tasks with unprecedented accuracy and efficiency. - Some of the potential applications and advancements include: -

- -

- 1. Improved Language Translation: With the ability to learn multiple languages simultaneously, large language models - could potentially become multilingual, enabling them to translate between different language pairs more effectively. -

-

- 2. Enhanced Conversational Systems: As these models improve their ability to understand and respond to complex questions - and requests, they will become increasingly adept at engaging in natural-sounding conversations with humans. -

-

- 3. Increased Efficiency in NLP Tasks: Large language models have the potential to automate many NLP tasks, such as text - summarization, sentiment analysis, and question answering, freeing up human resources for more complex and creative - tasks. -

-

- 4. Advancements in Creative Writing: Large language models could potentially be used to generate original and coherent - creative writing, such as poetry, short stories, or even entire novels. -

- -

- Conclusion -

- -

- Large language models have revolutionized the field of NLP, enabling us to perform complex linguistic operations with - unprecedented accuracy and efficiency. These models have numerous applications in language translation, text - summarization, chatbots, and creative writing, among others. As these models continue to advance in scale and - sophistication, they will undoubtedly lead to further advancements in NLP and potentially even transform the way we - interact with technology. -

- -

- Introduction -

- -

- The field of natural language processing (NLP) has witnessed tremendous growth and advancements in recent years, with - the emergence of large language models being a significant contributor to this progress. These models have - revolutionized the way we approach NLP tasks, enabling us to perform complex linguistic operations with unprecedented - accuracy and efficiency. In this article, we will delve into the concept of large language models, their architectures, - applications, and the future prospects in this field. -

- -

- What are Large Language Models? -

- -

- Large language models are artificial intelligence (AI) models that are trained on vast amounts of text data to generate - language representations that can be used for various NLP tasks. These models are designed to learn the patterns and - structures of language, enabling them to generate coherent and contextually appropriate text. The key characteristic of - large language models is their scale, with some models containing billions of parameters and trillions of words in their - training datasets. -

- -

- Architectures of Large Language Models -

- -

- There are several types of large language models, each with its unique architecture and applications. Some of the most - popular architectures include: -

- -

- 1. Transformers: This architecture is based on a self-attention mechanism that allows the model to weigh the importance - of different words in a sequence. Transformers have achieved state-of-the-art results in various NLP tasks, including - language translation and text generation. -

-

- 2. Recurrent Neural Networks (RNNs): RNNs are designed to process sequences of data, making them well-suited for tasks - such as language modeling, machine translation, and text summarization. These models use loops to feed information from - one time step to the next, enabling them to capture temporal dependencies in language. -

-

- 3. Generative Adversarial Networks (GANs): GANs consist of two components: a generator network that generates text, and - a discriminator network that evaluates the generated text and provides feedback to the generator. This adversarial - process enables the generator to produce more realistic and coherent text over time. -

- -

- Applications of Large Language Models -

- -

- Large language models have numerous applications in NLP, including: -

- -

- 1. Language Translation: Large language models can be trained on parallel corpora of source and target languages to - learn the patterns and structures of both languages. This enables them to generate high-quality translations that are - contextually appropriate. -

-

- 2. Text Summarization: These models can be used to summarize long documents or articles, extracting the most important - information and generating a concise summary. -

-

- 3. Chatbots and Conversational Systems: Large language models can be used to generate responses to user queries in - chatbots and conversational systems, enabling them to understand and respond to complex questions and requests. -

-

- 4. Language Generation: These models can be used to generate coherent and contextually appropriate text, such as - articles, stories, or even entire books. -

- -

- Future Prospects of Large Language Models -

- -

- The future prospects of large language models are bright and promising. As these models continue to advance in scale and - sophistication, they will enable us to perform a wide range of NLP tasks with unprecedented accuracy and efficiency. - Some of the potential applications and advancements include: -

- -

- 1. Improved Language Translation: With the ability to learn multiple languages simultaneously, large language models - could potentially become multilingual, enabling them to translate between different language pairs more effectively. -

-

- 2. Enhanced Conversational Systems: As these models improve their ability to understand and respond to complex questions - and requests, they will become increasingly adept at engaging in natural-sounding conversations with humans. -

-

- 3. Increased Efficiency in NLP Tasks: Large language models have the potential to automate many NLP tasks, such as text - summarization, sentiment analysis, and question answering, freeing up human resources for more complex and creative - tasks. -

-

- 4. Advancements in Creative Writing: Large language models could potentially be used to generate original and coherent - creative writing, such as poetry, short stories, or even entire novels. -

- -

- Conclusion -

- -

- Large language models have revolutionized the field of NLP, enabling us to perform complex linguistic operations with - unprecedented accuracy and efficiency. These models have numerous applications in language translation, text - summarization, chatbots, and creative writing, among others. As these models continue to advance in scale and - sophistication, they will undoubtedly lead to further advancements in NLP and potentially even transform the way we - interact with technology. -

- -

- Introduction -

- -

- The field of natural language processing (NLP) has witnessed tremendous growth and advancements in recent years, with - the emergence of large language models being a significant contributor to this progress. These models have - revolutionized the way we approach NLP tasks, enabling us to perform complex linguistic operations with unprecedented - accuracy and efficiency. In this article, we will delve into the concept of large language models, their architectures, - applications, and the future prospects in this field. -

- -

- What are Large Language Models? -

- -

- Large language models are artificial intelligence (AI) models that are trained on vast amounts of text data to generate - language representations that can be used for various NLP tasks. These models are designed to learn the patterns and - structures of language, enabling them to generate coherent and contextually appropriate text. The key characteristic of - large language models is their scale, with some models containing billions of parameters and trillions of words in their - training datasets. -

- -

- Architectures of Large Language Models -

- -

- There are several types of large language models, each with its unique architecture and applications. Some of the most - popular architectures include: -

- -

- 1. Transformers: This architecture is based on a self-attention mechanism that allows the model to weigh the importance - of different words in a sequence. Transformers have achieved state-of-the-art results in various NLP tasks, including - language translation and text generation. -

-

- 2. Recurrent Neural Networks (RNNs): RNNs are designed to process sequences of data, making them well-suited for tasks - such as language modeling, machine translation, and text summarization. These models use loops to feed information from - one time step to the next, enabling them to capture temporal dependencies in language. -

-

- 3. Generative Adversarial Networks (GANs): GANs consist of two components: a generator network that generates text, and - a discriminator network that evaluates the generated text and provides feedback to the generator. This adversarial - process enables the generator to produce more realistic and coherent text over time. -

- -

- Applications of Large Language Models -

- -

- Large language models have numerous applications in NLP, including: -

- -

- 1. Language Translation: Large language models can be trained on parallel corpora of source and target languages to - learn the patterns and structures of both languages. This enables them to generate high-quality translations that are - contextually appropriate. -

-

- 2. Text Summarization: These models can be used to summarize long documents or articles, extracting the most important - information and generating a concise summary. -

-

- 3. Chatbots and Conversational Systems: Large language models can be used to generate responses to user queries in - chatbots and conversational systems, enabling them to understand and respond to complex questions and requests. -

-

- 4. Language Generation: These models can be used to generate coherent and contextually appropriate text, such as - articles, stories, or even entire books. -

- -

- Future Prospects of Large Language Models -

- -

- The future prospects of large language models are bright and promising. As these models continue to advance in scale and - sophistication, they will enable us to perform a wide range of NLP tasks with unprecedented accuracy and efficiency. - Some of the potential applications and advancements include: -

- -

- 1. Improved Language Translation: With the ability to learn multiple languages simultaneously, large language models - could potentially become multilingual, enabling them to translate between different language pairs more effectively. -

-

- 2. Enhanced Conversational Systems: As these models improve their ability to understand and respond to complex questions - and requests, they will become increasingly adept at engaging in natural-sounding conversations with humans. -

-

- 3. Increased Efficiency in NLP Tasks: Large language models have the potential to automate many NLP tasks, such as text - summarization, sentiment analysis, and question answering, freeing up human resources for more complex and creative - tasks. -

-

- 4. Advancements in Creative Writing: Large language models could potentially be used to generate original and coherent - creative writing, such as poetry, short stories, or even entire novels. -

- -

- Conclusion -

- -

- Large language models have revolutionized the field of NLP, enabling us to perform complex linguistic operations with - unprecedented accuracy and efficiency. These models have numerous applications in language translation, text - summarization, chatbots, and creative writing, among others. As these models continue to advance in scale and - sophistication, they will undoubtedly lead to further advancements in NLP and potentially even transform the way we - interact with technology. -

- -

- Introduction -

- -

- The field of natural language processing (NLP) has witnessed tremendous growth and advancements in recent years, with - the emergence of large language models being a significant contributor to this progress. These models have - revolutionized the way we approach NLP tasks, enabling us to perform complex linguistic operations with unprecedented - accuracy and efficiency. In this article, we will delve into the concept of large language models, their architectures, - applications, and the future prospects in this field. -

- -

- What are Large Language Models? -

- -

- Large language models are artificial intelligence (AI) models that are trained on vast amounts of text data to generate - language representations that can be used for various NLP tasks. These models are designed to learn the patterns and - structures of language, enabling them to generate coherent and contextually appropriate text. The key characteristic of - large language models is their scale, with some models containing billions of parameters and trillions of words in their - training datasets. -

- -

- Architectures of Large Language Models -

- -

- There are several types of large language models, each with its unique architecture and applications. Some of the most - popular architectures include: -

- -

- 1. Transformers: This architecture is based on a self-attention mechanism that allows the model to weigh the importance - of different words in a sequence. Transformers have achieved state-of-the-art results in various NLP tasks, including - language translation and text generation. -

-

- 2. Recurrent Neural Networks (RNNs): RNNs are designed to process sequences of data, making them well-suited for tasks - such as language modeling, machine translation, and text summarization. These models use loops to feed information from - one time step to the next, enabling them to capture temporal dependencies in language. -

-

- 3. Generative Adversarial Networks (GANs): GANs consist of two components: a generator network that generates text, and - a discriminator network that evaluates the generated text and provides feedback to the generator. This adversarial - process enables the generator to produce more realistic and coherent text over time. -

- -

- Applications of Large Language Models -

- -

- Large language models have numerous applications in NLP, including: -

- -

- 1. Language Translation: Large language models can be trained on parallel corpora of source and target languages to - learn the patterns and structures of both languages. This enables them to generate high-quality translations that are - contextually appropriate. -

-

- 2. Text Summarization: These models can be used to summarize long documents or articles, extracting the most important - information and generating a concise summary. -

-

- 3. Chatbots and Conversational Systems: Large language models can be used to generate responses to user queries in - chatbots and conversational systems, enabling them to understand and respond to complex questions and requests. -

-

- 4. Language Generation: These models can be used to generate coherent and contextually appropriate text, such as - articles, stories, or even entire books. -

- -

- Future Prospects of Large Language Models -

- -

- The future prospects of large language models are bright and promising. As these models continue to advance in scale and - sophistication, they will enable us to perform a wide range of NLP tasks with unprecedented accuracy and efficiency. - Some of the potential applications and advancements include: -

- -

- 1. Improved Language Translation: With the ability to learn multiple languages simultaneously, large language models - could potentially become multilingual, enabling them to translate between different language pairs more effectively. -

-

- 2. Enhanced Conversational Systems: As these models improve their ability to understand and respond to complex questions - and requests, they will become increasingly adept at engaging in natural-sounding conversations with humans. -

-

- 3. Increased Efficiency in NLP Tasks: Large language models have the potential to automate many NLP tasks, such as text - summarization, sentiment analysis, and question answering, freeing up human resources for more complex and creative - tasks. -

-

- 4. Advancements in Creative Writing: Large language models could potentially be used to generate original and coherent - creative writing, such as poetry, short stories, or even entire novels. -

- -

- Conclusion -

- -

- Large language models have revolutionized the field of NLP, enabling us to perform complex linguistic operations with - unprecedented accuracy and efficiency. These models have numerous applications in language translation, text - summarization, chatbots, and creative writing, among others. As these models continue to advance in scale and - sophistication, they will undoubtedly lead to further advancements in NLP and potentially even transform the way we - interact with technology. -

- -

- Introduction -

- -

- The field of natural language processing (NLP) has witnessed tremendous growth and advancements in recent years, with - the emergence of large language models being a significant contributor to this progress. These models have - revolutionized the way we approach NLP tasks, enabling us to perform complex linguistic operations with unprecedented - accuracy and efficiency. In this article, we will delve into the concept of large language models, their architectures, - applications, and the future prospects in this field. -

- -

- What are Large Language Models? -

- -

- Large language models are artificial intelligence (AI) models that are trained on vast amounts of text data to generate - language representations that can be used for various NLP tasks. These models are designed to learn the patterns and - structures of language, enabling them to generate coherent and contextually appropriate text. The key characteristic of - large language models is their scale, with some models containing billions of parameters and trillions of words in their - training datasets. -

- -

- Architectures of Large Language Models -

- -

- There are several types of large language models, each with its unique architecture and applications. Some of the most - popular architectures include: -

- -

- 1. Transformers: This architecture is based on a self-attention mechanism that allows the model to weigh the importance - of different words in a sequence. Transformers have achieved state-of-the-art results in various NLP tasks, including - language translation and text generation. -

-

- 2. Recurrent Neural Networks (RNNs): RNNs are designed to process sequences of data, making them well-suited for tasks - such as language modeling, machine translation, and text summarization. These models use loops to feed information from - one time step to the next, enabling them to capture temporal dependencies in language. -

-

- 3. Generative Adversarial Networks (GANs): GANs consist of two components: a generator network that generates text, and - a discriminator network that evaluates the generated text and provides feedback to the generator. This adversarial - process enables the generator to produce more realistic and coherent text over time. -

- -

- Applications of Large Language Models -

- -

- Large language models have numerous applications in NLP, including: -

- -

- 1. Language Translation: Large language models can be trained on parallel corpora of source and target languages to - learn the patterns and structures of both languages. This enables them to generate high-quality translations that are - contextually appropriate. -

-

- 2. Text Summarization: These models can be used to summarize long documents or articles, extracting the most important - information and generating a concise summary. -

-

- 3. Chatbots and Conversational Systems: Large language models can be used to generate responses to user queries in - chatbots and conversational systems, enabling them to understand and respond to complex questions and requests. -

-

- 4. Language Generation: These models can be used to generate coherent and contextually appropriate text, such as - articles, stories, or even entire books. -

- -

- Future Prospects of Large Language Models -

- -

- The future prospects of large language models are bright and promising. As these models continue to advance in scale and - sophistication, they will enable us to perform a wide range of NLP tasks with unprecedented accuracy and efficiency. - Some of the potential applications and advancements include: -

- -

- 1. Improved Language Translation: With the ability to learn multiple languages simultaneously, large language models - could potentially become multilingual, enabling them to translate between different language pairs more effectively. -

-

- 2. Enhanced Conversational Systems: As these models improve their ability to understand and respond to complex questions - and requests, they will become increasingly adept at engaging in natural-sounding conversations with humans. -

-

- 3. Increased Efficiency in NLP Tasks: Large language models have the potential to automate many NLP tasks, such as text - summarization, sentiment analysis, and question answering, freeing up human resources for more complex and creative - tasks. -

-

- 4. Advancements in Creative Writing: Large language models could potentially be used to generate original and coherent - creative writing, such as poetry, short stories, or even entire novels. -

- -

- Conclusion -

- -

- Large language models have revolutionized the field of NLP, enabling us to perform complex linguistic operations with - unprecedented accuracy and efficiency. These models have numerous applications in language translation, text - summarization, chatbots, and creative writing, among others. As these models continue to advance in scale and - sophistication, they will undoubtedly lead to further advancements in NLP and potentially even transform the way we - interact with technology. -

- -

- Introduction -

- -

- The field of natural language processing (NLP) has witnessed tremendous growth and advancements in recent years, with - the emergence of large language models being a significant contributor to this progress. These models have - revolutionized the way we approach NLP tasks, enabling us to perform complex linguistic operations with unprecedented - accuracy and efficiency. In this article, we will delve into the concept of large language models, their architectures, - applications, and the future prospects in this field. -

- -

- What are Large Language Models? -

- -

- Large language models are artificial intelligence (AI) models that are trained on vast amounts of text data to generate - language representations that can be used for various NLP tasks. These models are designed to learn the patterns and - structures of language, enabling them to generate coherent and contextually appropriate text. The key characteristic of - large language models is their scale, with some models containing billions of parameters and trillions of words in their - training datasets. -

- -

- Architectures of Large Language Models -

- -

- There are several types of large language models, each with its unique architecture and applications. Some of the most - popular architectures include: -

- -

- 1. Transformers: This architecture is based on a self-attention mechanism that allows the model to weigh the importance - of different words in a sequence. Transformers have achieved state-of-the-art results in various NLP tasks, including - language translation and text generation. -

-

- 2. Recurrent Neural Networks (RNNs): RNNs are designed to process sequences of data, making them well-suited for tasks - such as language modeling, machine translation, and text summarization. These models use loops to feed information from - one time step to the next, enabling them to capture temporal dependencies in language. -

-

- 3. Generative Adversarial Networks (GANs): GANs consist of two components: a generator network that generates text, and - a discriminator network that evaluates the generated text and provides feedback to the generator. This adversarial - process enables the generator to produce more realistic and coherent text over time. -

- -

- Applications of Large Language Models -

- -

- Large language models have numerous applications in NLP, including: -

- -

- 1. Language Translation: Large language models can be trained on parallel corpora of source and target languages to - learn the patterns and structures of both languages. This enables them to generate high-quality translations that are - contextually appropriate. -

-

- 2. Text Summarization: These models can be used to summarize long documents or articles, extracting the most important - information and generating a concise summary. -

-

- 3. Chatbots and Conversational Systems: Large language models can be used to generate responses to user queries in - chatbots and conversational systems, enabling them to understand and respond to complex questions and requests. -

-

- 4. Language Generation: These models can be used to generate coherent and contextually appropriate text, such as - articles, stories, or even entire books. -

- -

- Future Prospects of Large Language Models -

- -

- The future prospects of large language models are bright and promising. As these models continue to advance in scale and - sophistication, they will enable us to perform a wide range of NLP tasks with unprecedented accuracy and efficiency. - Some of the potential applications and advancements include: -

- -

- 1. Improved Language Translation: With the ability to learn multiple languages simultaneously, large language models - could potentially become multilingual, enabling them to translate between different language pairs more effectively. -

-

- 2. Enhanced Conversational Systems: As these models improve their ability to understand and respond to complex questions - and requests, they will become increasingly adept at engaging in natural-sounding conversations with humans. -

-

- 3. Increased Efficiency in NLP Tasks: Large language models have the potential to automate many NLP tasks, such as text - summarization, sentiment analysis, and question answering, freeing up human resources for more complex and creative - tasks. -

-

- 4. Advancements in Creative Writing: Large language models could potentially be used to generate original and coherent - creative writing, such as poetry, short stories, or even entire novels. -

- -

- Conclusion -

- -

- Large language models have revolutionized the field of NLP, enabling us to perform complex linguistic operations with - unprecedented accuracy and efficiency. These models have numerous applications in language translation, text - summarization, chatbots, and creative writing, among others. As these models continue to advance in scale and - sophistication, they will undoubtedly lead to further advancements in NLP and potentially even transform the way we - interact with technology. -

- -

- Introduction -

- -

- The field of natural language processing (NLP) has witnessed tremendous growth and advancements in recent years, with - the emergence of large language models being a significant contributor to this progress. These models have - revolutionized the way we approach NLP tasks, enabling us to perform complex linguistic operations with unprecedented - accuracy and efficiency. In this article, we will delve into the concept of large language models, their architectures, - applications, and the future prospects in this field. -

- -

- What are Large Language Models? -

- -

- Large language models are artificial intelligence (AI) models that are trained on vast amounts of text data to generate - language representations that can be used for various NLP tasks. These models are designed to learn the patterns and - structures of language, enabling them to generate coherent and contextually appropriate text. The key characteristic of - large language models is their scale, with some models containing billions of parameters and trillions of words in their - training datasets. -

- -

- Architectures of Large Language Models -

- -

- There are several types of large language models, each with its unique architecture and applications. Some of the most - popular architectures include: -

- -

- 1. Transformers: This architecture is based on a self-attention mechanism that allows the model to weigh the importance - of different words in a sequence. Transformers have achieved state-of-the-art results in various NLP tasks, including - language translation and text generation. -

-

- 2. Recurrent Neural Networks (RNNs): RNNs are designed to process sequences of data, making them well-suited for tasks - such as language modeling, machine translation, and text summarization. These models use loops to feed information from - one time step to the next, enabling them to capture temporal dependencies in language. -

-

- 3. Generative Adversarial Networks (GANs): GANs consist of two components: a generator network that generates text, and - a discriminator network that evaluates the generated text and provides feedback to the generator. This adversarial - process enables the generator to produce more realistic and coherent text over time. -

- -

- Applications of Large Language Models -

- -

- Large language models have numerous applications in NLP, including: -

- -

- 1. Language Translation: Large language models can be trained on parallel corpora of source and target languages to - learn the patterns and structures of both languages. This enables them to generate high-quality translations that are - contextually appropriate. -

-

- 2. Text Summarization: These models can be used to summarize long documents or articles, extracting the most important - information and generating a concise summary. -

-

- 3. Chatbots and Conversational Systems: Large language models can be used to generate responses to user queries in - chatbots and conversational systems, enabling them to understand and respond to complex questions and requests. -

-

- 4. Language Generation: These models can be used to generate coherent and contextually appropriate text, such as - articles, stories, or even entire books. -

- -

- Future Prospects of Large Language Models -

- -

- The future prospects of large language models are bright and promising. As these models continue to advance in scale and - sophistication, they will enable us to perform a wide range of NLP tasks with unprecedented accuracy and efficiency. - Some of the potential applications and advancements include: -

- -

- 1. Improved Language Translation: With the ability to learn multiple languages simultaneously, large language models - could potentially become multilingual, enabling them to translate between different language pairs more effectively. -

-

- 2. Enhanced Conversational Systems: As these models improve their ability to understand and respond to complex questions - and requests, they will become increasingly adept at engaging in natural-sounding conversations with humans. -

-

- 3. Increased Efficiency in NLP Tasks: Large language models have the potential to automate many NLP tasks, such as text - summarization, sentiment analysis, and question answering, freeing up human resources for more complex and creative - tasks. -

-

- 4. Advancements in Creative Writing: Large language models could potentially be used to generate original and coherent - creative writing, such as poetry, short stories, or even entire novels. -

- -

- Conclusion -

- -

- Large language models have revolutionized the field of NLP, enabling us to perform complex linguistic operations with - unprecedented accuracy and efficiency. These models have numerous applications in language translation, text - summarization, chatbots, and creative writing, among others. As these models continue to advance in scale and - sophistication, they will undoubtedly lead to further advancements in NLP and potentially even transform the way we - interact with technology. -

- -

- Introduction -

- -

- The field of natural language processing (NLP) has witnessed tremendous growth and advancements in recent years, with - the emergence of large language models being a significant contributor to this progress. These models have - revolutionized the way we approach NLP tasks, enabling us to perform complex linguistic operations with unprecedented - accuracy and efficiency. In this article, we will delve into the concept of large language models, their architectures, - applications, and the future prospects in this field. -

- -

- What are Large Language Models? -

- -

- Large language models are artificial intelligence (AI) models that are trained on vast amounts of text data to generate - language representations that can be used for various NLP tasks. These models are designed to learn the patterns and - structures of language, enabling them to generate coherent and contextually appropriate text. The key characteristic of - large language models is their scale, with some models containing billions of parameters and trillions of words in their - training datasets. -

- -

- Architectures of Large Language Models -

- -

- There are several types of large language models, each with its unique architecture and applications. Some of the most - popular architectures include: -

- -

- 1. Transformers: This architecture is based on a self-attention mechanism that allows the model to weigh the importance - of different words in a sequence. Transformers have achieved state-of-the-art results in various NLP tasks, including - language translation and text generation. -

-

- 2. Recurrent Neural Networks (RNNs): RNNs are designed to process sequences of data, making them well-suited for tasks - such as language modeling, machine translation, and text summarization. These models use loops to feed information from - one time step to the next, enabling them to capture temporal dependencies in language. -

-

- 3. Generative Adversarial Networks (GANs): GANs consist of two components: a generator network that generates text, and - a discriminator network that evaluates the generated text and provides feedback to the generator. This adversarial - process enables the generator to produce more realistic and coherent text over time. -

- -

- Applications of Large Language Models -

- -

- Large language models have numerous applications in NLP, including: -

- -

- 1. Language Translation: Large language models can be trained on parallel corpora of source and target languages to - learn the patterns and structures of both languages. This enables them to generate high-quality translations that are - contextually appropriate. -

-

- 2. Text Summarization: These models can be used to summarize long documents or articles, extracting the most important - information and generating a concise summary. -

-

- 3. Chatbots and Conversational Systems: Large language models can be used to generate responses to user queries in - chatbots and conversational systems, enabling them to understand and respond to complex questions and requests. -

-

- 4. Language Generation: These models can be used to generate coherent and contextually appropriate text, such as - articles, stories, or even entire books. -

- -

- Future Prospects of Large Language Models -

- -

- The future prospects of large language models are bright and promising. As these models continue to advance in scale and - sophistication, they will enable us to perform a wide range of NLP tasks with unprecedented accuracy and efficiency. - Some of the potential applications and advancements include: -

- -

- 1. Improved Language Translation: With the ability to learn multiple languages simultaneously, large language models - could potentially become multilingual, enabling them to translate between different language pairs more effectively. -

-

- 2. Enhanced Conversational Systems: As these models improve their ability to understand and respond to complex questions - and requests, they will become increasingly adept at engaging in natural-sounding conversations with humans. -

-

- 3. Increased Efficiency in NLP Tasks: Large language models have the potential to automate many NLP tasks, such as text - summarization, sentiment analysis, and question answering, freeing up human resources for more complex and creative - tasks. -

-

- 4. Advancements in Creative Writing: Large language models could potentially be used to generate original and coherent - creative writing, such as poetry, short stories, or even entire novels. -

- -

- Conclusion -

- -

- Large language models have revolutionized the field of NLP, enabling us to perform complex linguistic operations with - unprecedented accuracy and efficiency. These models have numerous applications in language translation, text - summarization, chatbots, and creative writing, among others. As these models continue to advance in scale and - sophistication, they will undoubtedly lead to further advancements in NLP and potentially even transform the way we - interact with technology. -

- -

- Introduction -

- -

- The field of natural language processing (NLP) has witnessed tremendous growth and advancements in recent years, with - the emergence of large language models being a significant contributor to this progress. These models have - revolutionized the way we approach NLP tasks, enabling us to perform complex linguistic operations with unprecedented - accuracy and efficiency. In this article, we will delve into the concept of large language models, their architectures, - applications, and the future prospects in this field. -

- -

- What are Large Language Models? -

- -

- Large language models are artificial intelligence (AI) models that are trained on vast amounts of text data to generate - language representations that can be used for various NLP tasks. These models are designed to learn the patterns and - structures of language, enabling them to generate coherent and contextually appropriate text. The key characteristic of - large language models is their scale, with some models containing billions of parameters and trillions of words in their - training datasets. -

- -

- Architectures of Large Language Models -

- -

- There are several types of large language models, each with its unique architecture and applications. Some of the most - popular architectures include: -

- -

- 1. Transformers: This architecture is based on a self-attention mechanism that allows the model to weigh the importance - of different words in a sequence. Transformers have achieved state-of-the-art results in various NLP tasks, including - language translation and text generation. -

-

- 2. Recurrent Neural Networks (RNNs): RNNs are designed to process sequences of data, making them well-suited for tasks - such as language modeling, machine translation, and text summarization. These models use loops to feed information from - one time step to the next, enabling them to capture temporal dependencies in language. -

-

- 3. Generative Adversarial Networks (GANs): GANs consist of two components: a generator network that generates text, and - a discriminator network that evaluates the generated text and provides feedback to the generator. This adversarial - process enables the generator to produce more realistic and coherent text over time. -

- -

- Applications of Large Language Models -

- -

- Large language models have numerous applications in NLP, including: -

- -

- 1. Language Translation: Large language models can be trained on parallel corpora of source and target languages to - learn the patterns and structures of both languages. This enables them to generate high-quality translations that are - contextually appropriate. -

-

- 2. Text Summarization: These models can be used to summarize long documents or articles, extracting the most important - information and generating a concise summary. -

-

- 3. Chatbots and Conversational Systems: Large language models can be used to generate responses to user queries in - chatbots and conversational systems, enabling them to understand and respond to complex questions and requests. -

-

- 4. Language Generation: These models can be used to generate coherent and contextually appropriate text, such as - articles, stories, or even entire books. -

- -

- Future Prospects of Large Language Models -

- -

- The future prospects of large language models are bright and promising. As these models continue to advance in scale and - sophistication, they will enable us to perform a wide range of NLP tasks with unprecedented accuracy and efficiency. - Some of the potential applications and advancements include: -

- -

- 1. Improved Language Translation: With the ability to learn multiple languages simultaneously, large language models - could potentially become multilingual, enabling them to translate between different language pairs more effectively. -

-

- 2. Enhanced Conversational Systems: As these models improve their ability to understand and respond to complex questions - and requests, they will become increasingly adept at engaging in natural-sounding conversations with humans. -

-

- 3. Increased Efficiency in NLP Tasks: Large language models have the potential to automate many NLP tasks, such as text - summarization, sentiment analysis, and question answering, freeing up human resources for more complex and creative - tasks. -

-

- 4. Advancements in Creative Writing: Large language models could potentially be used to generate original and coherent - creative writing, such as poetry, short stories, or even entire novels. -

- -

- Conclusion -

- -

- Large language models have revolutionized the field of NLP, enabling us to perform complex linguistic operations with - unprecedented accuracy and efficiency. These models have numerous applications in language translation, text - summarization, chatbots, and creative writing, among others. As these models continue to advance in scale and - sophistication, they will undoubtedly lead to further advancements in NLP and potentially even transform the way we - interact with technology. -

- -

- Introduction -

- -

- The field of natural language processing (NLP) has witnessed tremendous growth and advancements in recent years, with - the emergence of large language models being a significant contributor to this progress. These models have - revolutionized the way we approach NLP tasks, enabling us to perform complex linguistic operations with unprecedented - accuracy and efficiency. In this article, we will delve into the concept of large language models, their architectures, - applications, and the future prospects in this field. -

- -

- What are Large Language Models? -

- -

- Large language models are artificial intelligence (AI) models that are trained on vast amounts of text data to generate - language representations that can be used for various NLP tasks. These models are designed to learn the patterns and - structures of language, enabling them to generate coherent and contextually appropriate text. The key characteristic of - large language models is their scale, with some models containing billions of parameters and trillions of words in their - training datasets. -

- -

- Architectures of Large Language Models -

- -

- There are several types of large language models, each with its unique architecture and applications. Some of the most - popular architectures include: -

- -

- 1. Transformers: This architecture is based on a self-attention mechanism that allows the model to weigh the importance - of different words in a sequence. Transformers have achieved state-of-the-art results in various NLP tasks, including - language translation and text generation. -

-

- 2. Recurrent Neural Networks (RNNs): RNNs are designed to process sequences of data, making them well-suited for tasks - such as language modeling, machine translation, and text summarization. These models use loops to feed information from - one time step to the next, enabling them to capture temporal dependencies in language. -

-

- 3. Generative Adversarial Networks (GANs): GANs consist of two components: a generator network that generates text, and - a discriminator network that evaluates the generated text and provides feedback to the generator. This adversarial - process enables the generator to produce more realistic and coherent text over time. -

- -

- Applications of Large Language Models -

- -

- Large language models have numerous applications in NLP, including: -

- -

- 1. Language Translation: Large language models can be trained on parallel corpora of source and target languages to - learn the patterns and structures of both languages. This enables them to generate high-quality translations that are - contextually appropriate. -

-

- 2. Text Summarization: These models can be used to summarize long documents or articles, extracting the most important - information and generating a concise summary. -

-

- 3. Chatbots and Conversational Systems: Large language models can be used to generate responses to user queries in - chatbots and conversational systems, enabling them to understand and respond to complex questions and requests. -

-

- 4. Language Generation: These models can be used to generate coherent and contextually appropriate text, such as - articles, stories, or even entire books. -

- -

- Future Prospects of Large Language Models -

- -

- The future prospects of large language models are bright and promising. As these models continue to advance in scale and - sophistication, they will enable us to perform a wide range of NLP tasks with unprecedented accuracy and efficiency. - Some of the potential applications and advancements include: -

- -

- 1. Improved Language Translation: With the ability to learn multiple languages simultaneously, large language models - could potentially become multilingual, enabling them to translate between different language pairs more effectively. -

-

- 2. Enhanced Conversational Systems: As these models improve their ability to understand and respond to complex questions - and requests, they will become increasingly adept at engaging in natural-sounding conversations with humans. -

-

- 3. Increased Efficiency in NLP Tasks: Large language models have the potential to automate many NLP tasks, such as text - summarization, sentiment analysis, and question answering, freeing up human resources for more complex and creative - tasks. -

-

- 4. Advancements in Creative Writing: Large language models could potentially be used to generate original and coherent - creative writing, such as poetry, short stories, or even entire novels. -

- -

- Conclusion -

- -

- Large language models have revolutionized the field of NLP, enabling us to perform complex linguistic operations with - unprecedented accuracy and efficiency. These models have numerous applications in language translation, text - summarization, chatbots, and creative writing, among others. As these models continue to advance in scale and - sophistication, they will undoubtedly lead to further advancements in NLP and potentially even transform the way we - interact with technology. -

- -

- Introduction -

- -

- The field of natural language processing (NLP) has witnessed tremendous growth and advancements in recent years, with - the emergence of large language models being a significant contributor to this progress. These models have - revolutionized the way we approach NLP tasks, enabling us to perform complex linguistic operations with unprecedented - accuracy and efficiency. In this article, we will delve into the concept of large language models, their architectures, - applications, and the future prospects in this field. -

- -

- What are Large Language Models? -

- -

- Large language models are artificial intelligence (AI) models that are trained on vast amounts of text data to generate - language representations that can be used for various NLP tasks. These models are designed to learn the patterns and - structures of language, enabling them to generate coherent and contextually appropriate text. The key characteristic of - large language models is their scale, with some models containing billions of parameters and trillions of words in their - training datasets. -

- -

- Architectures of Large Language Models -

- -

- There are several types of large language models, each with its unique architecture and applications. Some of the most - popular architectures include: -

- -

- 1. Transformers: This architecture is based on a self-attention mechanism that allows the model to weigh the importance - of different words in a sequence. Transformers have achieved state-of-the-art results in various NLP tasks, including - language translation and text generation. -

-

- 2. Recurrent Neural Networks (RNNs): RNNs are designed to process sequences of data, making them well-suited for tasks - such as language modeling, machine translation, and text summarization. These models use loops to feed information from - one time step to the next, enabling them to capture temporal dependencies in language. -

-

- 3. Generative Adversarial Networks (GANs): GANs consist of two components: a generator network that generates text, and - a discriminator network that evaluates the generated text and provides feedback to the generator. This adversarial - process enables the generator to produce more realistic and coherent text over time. -

- -

- Applications of Large Language Models -

- -

- Large language models have numerous applications in NLP, including: -

- -

- 1. Language Translation: Large language models can be trained on parallel corpora of source and target languages to - learn the patterns and structures of both languages. This enables them to generate high-quality translations that are - contextually appropriate. -

-

- 2. Text Summarization: These models can be used to summarize long documents or articles, extracting the most important - information and generating a concise summary. -

-

- 3. Chatbots and Conversational Systems: Large language models can be used to generate responses to user queries in - chatbots and conversational systems, enabling them to understand and respond to complex questions and requests. -

-

- 4. Language Generation: These models can be used to generate coherent and contextually appropriate text, such as - articles, stories, or even entire books. -

- -

- Future Prospects of Large Language Models -

- -

- The future prospects of large language models are bright and promising. As these models continue to advance in scale and - sophistication, they will enable us to perform a wide range of NLP tasks with unprecedented accuracy and efficiency. - Some of the potential applications and advancements include: -

- -

- 1. Improved Language Translation: With the ability to learn multiple languages simultaneously, large language models - could potentially become multilingual, enabling them to translate between different language pairs more effectively. -

-

- 2. Enhanced Conversational Systems: As these models improve their ability to understand and respond to complex questions - and requests, they will become increasingly adept at engaging in natural-sounding conversations with humans. -

-

- 3. Increased Efficiency in NLP Tasks: Large language models have the potential to automate many NLP tasks, such as text - summarization, sentiment analysis, and question answering, freeing up human resources for more complex and creative - tasks. -

-

- 4. Advancements in Creative Writing: Large language models could potentially be used to generate original and coherent - creative writing, such as poetry, short stories, or even entire novels. -

- -

- Conclusion -

- -

- Large language models have revolutionized the field of NLP, enabling us to perform complex linguistic operations with - unprecedented accuracy and efficiency. These models have numerous applications in language translation, text - summarization, chatbots, and creative writing, among others. As these models continue to advance in scale and - sophistication, they will undoubtedly lead to further advancements in NLP and potentially even transform the way we - interact with technology. -

- -

- Introduction -

- -

- The field of natural language processing (NLP) has witnessed tremendous growth and advancements in recent years, with - the emergence of large language models being a significant contributor to this progress. These models have - revolutionized the way we approach NLP tasks, enabling us to perform complex linguistic operations with unprecedented - accuracy and efficiency. In this article, we will delve into the concept of large language models, their architectures, - applications, and the future prospects in this field. -

- -

- What are Large Language Models? -

- -

- Large language models are artificial intelligence (AI) models that are trained on vast amounts of text data to generate - language representations that can be used for various NLP tasks. These models are designed to learn the patterns and - structures of language, enabling them to generate coherent and contextually appropriate text. The key characteristic of - large language models is their scale, with some models containing billions of parameters and trillions of words in their - training datasets. -

- -

- Architectures of Large Language Models -

- -

- There are several types of large language models, each with its unique architecture and applications. Some of the most - popular architectures include: -

- -

- 1. Transformers: This architecture is based on a self-attention mechanism that allows the model to weigh the importance - of different words in a sequence. Transformers have achieved state-of-the-art results in various NLP tasks, including - language translation and text generation. -

-

- 2. Recurrent Neural Networks (RNNs): RNNs are designed to process sequences of data, making them well-suited for tasks - such as language modeling, machine translation, and text summarization. These models use loops to feed information from - one time step to the next, enabling them to capture temporal dependencies in language. -

-

- 3. Generative Adversarial Networks (GANs): GANs consist of two components: a generator network that generates text, and - a discriminator network that evaluates the generated text and provides feedback to the generator. This adversarial - process enables the generator to produce more realistic and coherent text over time. -

- -

- Applications of Large Language Models -

- -

- Large language models have numerous applications in NLP, including: -

- -

- 1. Language Translation: Large language models can be trained on parallel corpora of source and target languages to - learn the patterns and structures of both languages. This enables them to generate high-quality translations that are - contextually appropriate. -

-

- 2. Text Summarization: These models can be used to summarize long documents or articles, extracting the most important - information and generating a concise summary. -

-

- 3. Chatbots and Conversational Systems: Large language models can be used to generate responses to user queries in - chatbots and conversational systems, enabling them to understand and respond to complex questions and requests. -

-

- 4. Language Generation: These models can be used to generate coherent and contextually appropriate text, such as - articles, stories, or even entire books. -

- -

- Future Prospects of Large Language Models -

- -

- The future prospects of large language models are bright and promising. As these models continue to advance in scale and - sophistication, they will enable us to perform a wide range of NLP tasks with unprecedented accuracy and efficiency. - Some of the potential applications and advancements include: -

- -

- 1. Improved Language Translation: With the ability to learn multiple languages simultaneously, large language models - could potentially become multilingual, enabling them to translate between different language pairs more effectively. -

-

- 2. Enhanced Conversational Systems: As these models improve their ability to understand and respond to complex questions - and requests, they will become increasingly adept at engaging in natural-sounding conversations with humans. -

-

- 3. Increased Efficiency in NLP Tasks: Large language models have the potential to automate many NLP tasks, such as text - summarization, sentiment analysis, and question answering, freeing up human resources for more complex and creative - tasks. -

-

- 4. Advancements in Creative Writing: Large language models could potentially be used to generate original and coherent - creative writing, such as poetry, short stories, or even entire novels. -

- -

- Conclusion -

- -

- Large language models have revolutionized the field of NLP, enabling us to perform complex linguistic operations with - unprecedented accuracy and efficiency. These models have numerous applications in language translation, text - summarization, chatbots, and creative writing, among others. As these models continue to advance in scale and - sophistication, they will undoubtedly lead to further advancements in NLP and potentially even transform the way we - interact with technology. -

- -

- Introduction -

- -

- The field of natural language processing (NLP) has witnessed tremendous growth and advancements in recent years, with - the emergence of large language models being a significant contributor to this progress. These models have - revolutionized the way we approach NLP tasks, enabling us to perform complex linguistic operations with unprecedented - accuracy and efficiency. In this article, we will delve into the concept of large language models, their architectures, - applications, and the future prospects in this field. -

- -

- What are Large Language Models? -

- -

- Large language models are artificial intelligence (AI) models that are trained on vast amounts of text data to generate - language representations that can be used for various NLP tasks. These models are designed to learn the patterns and - structures of language, enabling them to generate coherent and contextually appropriate text. The key characteristic of - large language models is their scale, with some models containing billions of parameters and trillions of words in their - training datasets. -

- -

- Architectures of Large Language Models -

- -

- There are several types of large language models, each with its unique architecture and applications. Some of the most - popular architectures include: -

- -

- 1. Transformers: This architecture is based on a self-attention mechanism that allows the model to weigh the importance - of different words in a sequence. Transformers have achieved state-of-the-art results in various NLP tasks, including - language translation and text generation. -

-

- 2. Recurrent Neural Networks (RNNs): RNNs are designed to process sequences of data, making them well-suited for tasks - such as language modeling, machine translation, and text summarization. These models use loops to feed information from - one time step to the next, enabling them to capture temporal dependencies in language. -

-

- 3. Generative Adversarial Networks (GANs): GANs consist of two components: a generator network that generates text, and - a discriminator network that evaluates the generated text and provides feedback to the generator. This adversarial - process enables the generator to produce more realistic and coherent text over time. -

- -

- Applications of Large Language Models -

- -

- Large language models have numerous applications in NLP, including: -

- -

- 1. Language Translation: Large language models can be trained on parallel corpora of source and target languages to - learn the patterns and structures of both languages. This enables them to generate high-quality translations that are - contextually appropriate. -

-

- 2. Text Summarization: These models can be used to summarize long documents or articles, extracting the most important - information and generating a concise summary. -

-

- 3. Chatbots and Conversational Systems: Large language models can be used to generate responses to user queries in - chatbots and conversational systems, enabling them to understand and respond to complex questions and requests. -

-

- 4. Language Generation: These models can be used to generate coherent and contextually appropriate text, such as - articles, stories, or even entire books. -

- -

- Future Prospects of Large Language Models -

- -

- The future prospects of large language models are bright and promising. As these models continue to advance in scale and - sophistication, they will enable us to perform a wide range of NLP tasks with unprecedented accuracy and efficiency. - Some of the potential applications and advancements include: -

- -

- 1. Improved Language Translation: With the ability to learn multiple languages simultaneously, large language models - could potentially become multilingual, enabling them to translate between different language pairs more effectively. -

-

- 2. Enhanced Conversational Systems: As these models improve their ability to understand and respond to complex questions - and requests, they will become increasingly adept at engaging in natural-sounding conversations with humans. -

-

- 3. Increased Efficiency in NLP Tasks: Large language models have the potential to automate many NLP tasks, such as text - summarization, sentiment analysis, and question answering, freeing up human resources for more complex and creative - tasks. -

-

- 4. Advancements in Creative Writing: Large language models could potentially be used to generate original and coherent - creative writing, such as poetry, short stories, or even entire novels. -

- -

- Conclusion -

- -

- Large language models have revolutionized the field of NLP, enabling us to perform complex linguistic operations with - unprecedented accuracy and efficiency. These models have numerous applications in language translation, text - summarization, chatbots, and creative writing, among others. As these models continue to advance in scale and - sophistication, they will undoubtedly lead to further advancements in NLP and potentially even transform the way we - interact with technology. -

- -

- Introduction -

- -

- The field of natural language processing (NLP) has witnessed tremendous growth and advancements in recent years, with - the emergence of large language models being a significant contributor to this progress. These models have - revolutionized the way we approach NLP tasks, enabling us to perform complex linguistic operations with unprecedented - accuracy and efficiency. In this article, we will delve into the concept of large language models, their architectures, - applications, and the future prospects in this field. -

- -

- What are Large Language Models? -

- -

- Large language models are artificial intelligence (AI) models that are trained on vast amounts of text data to generate - language representations that can be used for various NLP tasks. These models are designed to learn the patterns and - structures of language, enabling them to generate coherent and contextually appropriate text. The key characteristic of - large language models is their scale, with some models containing billions of parameters and trillions of words in their - training datasets. -

- -

- Architectures of Large Language Models -

- -

- There are several types of large language models, each with its unique architecture and applications. Some of the most - popular architectures include: -

- -

- 1. Transformers: This architecture is based on a self-attention mechanism that allows the model to weigh the importance - of different words in a sequence. Transformers have achieved state-of-the-art results in various NLP tasks, including - language translation and text generation. -

-

- 2. Recurrent Neural Networks (RNNs): RNNs are designed to process sequences of data, making them well-suited for tasks - such as language modeling, machine translation, and text summarization. These models use loops to feed information from - one time step to the next, enabling them to capture temporal dependencies in language. -

-

- 3. Generative Adversarial Networks (GANs): GANs consist of two components: a generator network that generates text, and - a discriminator network that evaluates the generated text and provides feedback to the generator. This adversarial - process enables the generator to produce more realistic and coherent text over time. -

- -

- Applications of Large Language Models -

- -

- Large language models have numerous applications in NLP, including: -

- -

- 1. Language Translation: Large language models can be trained on parallel corpora of source and target languages to - learn the patterns and structures of both languages. This enables them to generate high-quality translations that are - contextually appropriate. -

-

- 2. Text Summarization: These models can be used to summarize long documents or articles, extracting the most important - information and generating a concise summary. -

-

- 3. Chatbots and Conversational Systems: Large language models can be used to generate responses to user queries in - chatbots and conversational systems, enabling them to understand and respond to complex questions and requests. -

-

- 4. Language Generation: These models can be used to generate coherent and contextually appropriate text, such as - articles, stories, or even entire books. -

- -

- Future Prospects of Large Language Models -

- -

- The future prospects of large language models are bright and promising. As these models continue to advance in scale and - sophistication, they will enable us to perform a wide range of NLP tasks with unprecedented accuracy and efficiency. - Some of the potential applications and advancements include: -

- -

- 1. Improved Language Translation: With the ability to learn multiple languages simultaneously, large language models - could potentially become multilingual, enabling them to translate between different language pairs more effectively. -

-

- 2. Enhanced Conversational Systems: As these models improve their ability to understand and respond to complex questions - and requests, they will become increasingly adept at engaging in natural-sounding conversations with humans. -

-

- 3. Increased Efficiency in NLP Tasks: Large language models have the potential to automate many NLP tasks, such as text - summarization, sentiment analysis, and question answering, freeing up human resources for more complex and creative - tasks. -

-

- 4. Advancements in Creative Writing: Large language models could potentially be used to generate original and coherent - creative writing, such as poetry, short stories, or even entire novels. -

- -

- Conclusion -

- -

- Large language models have revolutionized the field of NLP, enabling us to perform complex linguistic operations with - unprecedented accuracy and efficiency. These models have numerous applications in language translation, text - summarization, chatbots, and creative writing, among others. As these models continue to advance in scale and - sophistication, they will undoubtedly lead to further advancements in NLP and potentially even transform the way we - interact with technology. -

- -

- Introduction -

- -

- The field of natural language processing (NLP) has witnessed tremendous growth and advancements in recent years, with - the emergence of large language models being a significant contributor to this progress. These models have - revolutionized the way we approach NLP tasks, enabling us to perform complex linguistic operations with unprecedented - accuracy and efficiency. In this article, we will delve into the concept of large language models, their architectures, - applications, and the future prospects in this field. -

- -

- What are Large Language Models? -

- -

- Large language models are artificial intelligence (AI) models that are trained on vast amounts of text data to generate - language representations that can be used for various NLP tasks. These models are designed to learn the patterns and - structures of language, enabling them to generate coherent and contextually appropriate text. The key characteristic of - large language models is their scale, with some models containing billions of parameters and trillions of words in their - training datasets. -

- -

- Architectures of Large Language Models -

- -

- There are several types of large language models, each with its unique architecture and applications. Some of the most - popular architectures include: -

- -

- 1. Transformers: This architecture is based on a self-attention mechanism that allows the model to weigh the importance - of different words in a sequence. Transformers have achieved state-of-the-art results in various NLP tasks, including - language translation and text generation. -

-

- 2. Recurrent Neural Networks (RNNs): RNNs are designed to process sequences of data, making them well-suited for tasks - such as language modeling, machine translation, and text summarization. These models use loops to feed information from - one time step to the next, enabling them to capture temporal dependencies in language. -

-

- 3. Generative Adversarial Networks (GANs): GANs consist of two components: a generator network that generates text, and - a discriminator network that evaluates the generated text and provides feedback to the generator. This adversarial - process enables the generator to produce more realistic and coherent text over time. -

- -

- Applications of Large Language Models -

- -

- Large language models have numerous applications in NLP, including: -

- -

- 1. Language Translation: Large language models can be trained on parallel corpora of source and target languages to - learn the patterns and structures of both languages. This enables them to generate high-quality translations that are - contextually appropriate. -

-

- 2. Text Summarization: These models can be used to summarize long documents or articles, extracting the most important - information and generating a concise summary. -

-

- 3. Chatbots and Conversational Systems: Large language models can be used to generate responses to user queries in - chatbots and conversational systems, enabling them to understand and respond to complex questions and requests. -

-

- 4. Language Generation: These models can be used to generate coherent and contextually appropriate text, such as - articles, stories, or even entire books. -

- -

- Future Prospects of Large Language Models -

- -

- The future prospects of large language models are bright and promising. As these models continue to advance in scale and - sophistication, they will enable us to perform a wide range of NLP tasks with unprecedented accuracy and efficiency. - Some of the potential applications and advancements include: -

- -

- 1. Improved Language Translation: With the ability to learn multiple languages simultaneously, large language models - could potentially become multilingual, enabling them to translate between different language pairs more effectively. -

-

- 2. Enhanced Conversational Systems: As these models improve their ability to understand and respond to complex questions - and requests, they will become increasingly adept at engaging in natural-sounding conversations with humans. -

-

- 3. Increased Efficiency in NLP Tasks: Large language models have the potential to automate many NLP tasks, such as text - summarization, sentiment analysis, and question answering, freeing up human resources for more complex and creative - tasks. -

-

- 4. Advancements in Creative Writing: Large language models could potentially be used to generate original and coherent - creative writing, such as poetry, short stories, or even entire novels. -

- -

- Conclusion -

- -

- Large language models have revolutionized the field of NLP, enabling us to perform complex linguistic operations with - unprecedented accuracy and efficiency. These models have numerous applications in language translation, text - summarization, chatbots, and creative writing, among others. As these models continue to advance in scale and - sophistication, they will undoubtedly lead to further advancements in NLP and potentially even transform the way we - interact with technology. -

- -

- Introduction -

- -

- The field of natural language processing (NLP) has witnessed tremendous growth and advancements in recent years, with - the emergence of large language models being a significant contributor to this progress. These models have - revolutionized the way we approach NLP tasks, enabling us to perform complex linguistic operations with unprecedented - accuracy and efficiency. In this article, we will delve into the concept of large language models, their architectures, - applications, and the future prospects in this field. -

- -

- What are Large Language Models? -

- -

- Large language models are artificial intelligence (AI) models that are trained on vast amounts of text data to generate - language representations that can be used for various NLP tasks. These models are designed to learn the patterns and - structures of language, enabling them to generate coherent and contextually appropriate text. The key characteristic of - large language models is their scale, with some models containing billions of parameters and trillions of words in their - training datasets. -

- -

- Architectures of Large Language Models -

- -

- There are several types of large language models, each with its unique architecture and applications. Some of the most - popular architectures include: -

- -

- 1. Transformers: This architecture is based on a self-attention mechanism that allows the model to weigh the importance - of different words in a sequence. Transformers have achieved state-of-the-art results in various NLP tasks, including - language translation and text generation. -

-

- 2. Recurrent Neural Networks (RNNs): RNNs are designed to process sequences of data, making them well-suited for tasks - such as language modeling, machine translation, and text summarization. These models use loops to feed information from - one time step to the next, enabling them to capture temporal dependencies in language. -

-

- 3. Generative Adversarial Networks (GANs): GANs consist of two components: a generator network that generates text, and - a discriminator network that evaluates the generated text and provides feedback to the generator. This adversarial - process enables the generator to produce more realistic and coherent text over time. -

- -

- Applications of Large Language Models -

- -

- Large language models have numerous applications in NLP, including: -

- -

- 1. Language Translation: Large language models can be trained on parallel corpora of source and target languages to - learn the patterns and structures of both languages. This enables them to generate high-quality translations that are - contextually appropriate. -

-

- 2. Text Summarization: These models can be used to summarize long documents or articles, extracting the most important - information and generating a concise summary. -

-

- 3. Chatbots and Conversational Systems: Large language models can be used to generate responses to user queries in - chatbots and conversational systems, enabling them to understand and respond to complex questions and requests. -

-

- 4. Language Generation: These models can be used to generate coherent and contextually appropriate text, such as - articles, stories, or even entire books. -

- -

- Future Prospects of Large Language Models -

- -

- The future prospects of large language models are bright and promising. As these models continue to advance in scale and - sophistication, they will enable us to perform a wide range of NLP tasks with unprecedented accuracy and efficiency. - Some of the potential applications and advancements include: -

- -

- 1. Improved Language Translation: With the ability to learn multiple languages simultaneously, large language models - could potentially become multilingual, enabling them to translate between different language pairs more effectively. -

-

- 2. Enhanced Conversational Systems: As these models improve their ability to understand and respond to complex questions - and requests, they will become increasingly adept at engaging in natural-sounding conversations with humans. -

-

- 3. Increased Efficiency in NLP Tasks: Large language models have the potential to automate many NLP tasks, such as text - summarization, sentiment analysis, and question answering, freeing up human resources for more complex and creative - tasks. -

-

- 4. Advancements in Creative Writing: Large language models could potentially be used to generate original and coherent - creative writing, such as poetry, short stories, or even entire novels. -

- -

- Conclusion -

- -

- Large language models have revolutionized the field of NLP, enabling us to perform complex linguistic operations with - unprecedented accuracy and efficiency. These models have numerous applications in language translation, text - summarization, chatbots, and creative writing, among others. As these models continue to advance in scale and - sophistication, they will undoubtedly lead to further advancements in NLP and potentially even transform the way we - interact with technology. -

- -

- Introduction -

- -

- The field of natural language processing (NLP) has witnessed tremendous growth and advancements in recent years, with - the emergence of large language models being a significant contributor to this progress. These models have - revolutionized the way we approach NLP tasks, enabling us to perform complex linguistic operations with unprecedented - accuracy and efficiency. In this article, we will delve into the concept of large language models, their architectures, - applications, and the future prospects in this field. -

- -

- What are Large Language Models? -

- -

- Large language models are artificial intelligence (AI) models that are trained on vast amounts of text data to generate - language representations that can be used for various NLP tasks. These models are designed to learn the patterns and - structures of language, enabling them to generate coherent and contextually appropriate text. The key characteristic of - large language models is their scale, with some models containing billions of parameters and trillions of words in their - training datasets. -

- -

- Architectures of Large Language Models -

- -

- There are several types of large language models, each with its unique architecture and applications. Some of the most - popular architectures include: -

- -

- 1. Transformers: This architecture is based on a self-attention mechanism that allows the model to weigh the importance - of different words in a sequence. Transformers have achieved state-of-the-art results in various NLP tasks, including - language translation and text generation. -

-

- 2. Recurrent Neural Networks (RNNs): RNNs are designed to process sequences of data, making them well-suited for tasks - such as language modeling, machine translation, and text summarization. These models use loops to feed information from - one time step to the next, enabling them to capture temporal dependencies in language. -

-

- 3. Generative Adversarial Networks (GANs): GANs consist of two components: a generator network that generates text, and - a discriminator network that evaluates the generated text and provides feedback to the generator. This adversarial - process enables the generator to produce more realistic and coherent text over time. -

- -

- Applications of Large Language Models -

- -

- Large language models have numerous applications in NLP, including: -

- -

- 1. Language Translation: Large language models can be trained on parallel corpora of source and target languages to - learn the patterns and structures of both languages. This enables them to generate high-quality translations that are - contextually appropriate. -

-

- 2. Text Summarization: These models can be used to summarize long documents or articles, extracting the most important - information and generating a concise summary. -

-

- 3. Chatbots and Conversational Systems: Large language models can be used to generate responses to user queries in - chatbots and conversational systems, enabling them to understand and respond to complex questions and requests. -

-

- 4. Language Generation: These models can be used to generate coherent and contextually appropriate text, such as - articles, stories, or even entire books. -

- -

- Future Prospects of Large Language Models -

- -

- The future prospects of large language models are bright and promising. As these models continue to advance in scale and - sophistication, they will enable us to perform a wide range of NLP tasks with unprecedented accuracy and efficiency. - Some of the potential applications and advancements include: -

- -

- 1. Improved Language Translation: With the ability to learn multiple languages simultaneously, large language models - could potentially become multilingual, enabling them to translate between different language pairs more effectively. -

-

- 2. Enhanced Conversational Systems: As these models improve their ability to understand and respond to complex questions - and requests, they will become increasingly adept at engaging in natural-sounding conversations with humans. -

-

- 3. Increased Efficiency in NLP Tasks: Large language models have the potential to automate many NLP tasks, such as text - summarization, sentiment analysis, and question answering, freeing up human resources for more complex and creative - tasks. -

-

- 4. Advancements in Creative Writing: Large language models could potentially be used to generate original and coherent - creative writing, such as poetry, short stories, or even entire novels. -

- -

- Conclusion -

- -

- Large language models have revolutionized the field of NLP, enabling us to perform complex linguistic operations with - unprecedented accuracy and efficiency. These models have numerous applications in language translation, text - summarization, chatbots, and creative writing, among others. As these models continue to advance in scale and - sophistication, they will undoubtedly lead to further advancements in NLP and potentially even transform the way we - interact with technology. -

- -

- Introduction -

- -

- The field of natural language processing (NLP) has witnessed tremendous growth and advancements in recent years, with - the emergence of large language models being a significant contributor to this progress. These models have - revolutionized the way we approach NLP tasks, enabling us to perform complex linguistic operations with unprecedented - accuracy and efficiency. In this article, we will delve into the concept of large language models, their architectures, - applications, and the future prospects in this field. -

- -

- What are Large Language Models? -

- -

- Large language models are artificial intelligence (AI) models that are trained on vast amounts of text data to generate - language representations that can be used for various NLP tasks. These models are designed to learn the patterns and - structures of language, enabling them to generate coherent and contextually appropriate text. The key characteristic of - large language models is their scale, with some models containing billions of parameters and trillions of words in their - training datasets. -

- -

- Architectures of Large Language Models -

- -

- There are several types of large language models, each with its unique architecture and applications. Some of the most - popular architectures include: -

- -

- 1. Transformers: This architecture is based on a self-attention mechanism that allows the model to weigh the importance - of different words in a sequence. Transformers have achieved state-of-the-art results in various NLP tasks, including - language translation and text generation. -

-

- 2. Recurrent Neural Networks (RNNs): RNNs are designed to process sequences of data, making them well-suited for tasks - such as language modeling, machine translation, and text summarization. These models use loops to feed information from - one time step to the next, enabling them to capture temporal dependencies in language. -

-

- 3. Generative Adversarial Networks (GANs): GANs consist of two components: a generator network that generates text, and - a discriminator network that evaluates the generated text and provides feedback to the generator. This adversarial - process enables the generator to produce more realistic and coherent text over time. -

- -

- Applications of Large Language Models -

- -

- Large language models have numerous applications in NLP, including: -

- -

- 1. Language Translation: Large language models can be trained on parallel corpora of source and target languages to - learn the patterns and structures of both languages. This enables them to generate high-quality translations that are - contextually appropriate. -

-

- 2. Text Summarization: These models can be used to summarize long documents or articles, extracting the most important - information and generating a concise summary. -

-

- 3. Chatbots and Conversational Systems: Large language models can be used to generate responses to user queries in - chatbots and conversational systems, enabling them to understand and respond to complex questions and requests. -

-

- 4. Language Generation: These models can be used to generate coherent and contextually appropriate text, such as - articles, stories, or even entire books. -

- -

- Future Prospects of Large Language Models -

- -

- The future prospects of large language models are bright and promising. As these models continue to advance in scale and - sophistication, they will enable us to perform a wide range of NLP tasks with unprecedented accuracy and efficiency. - Some of the potential applications and advancements include: -

- -

- 1. Improved Language Translation: With the ability to learn multiple languages simultaneously, large language models - could potentially become multilingual, enabling them to translate between different language pairs more effectively. -

-

- 2. Enhanced Conversational Systems: As these models improve their ability to understand and respond to complex questions - and requests, they will become increasingly adept at engaging in natural-sounding conversations with humans. -

-

- 3. Increased Efficiency in NLP Tasks: Large language models have the potential to automate many NLP tasks, such as text - summarization, sentiment analysis, and question answering, freeing up human resources for more complex and creative - tasks. -

-

- 4. Advancements in Creative Writing: Large language models could potentially be used to generate original and coherent - creative writing, such as poetry, short stories, or even entire novels. -

- -

- Conclusion -

- -

- Large language models have revolutionized the field of NLP, enabling us to perform complex linguistic operations with - unprecedented accuracy and efficiency. These models have numerous applications in language translation, text - summarization, chatbots, and creative writing, among others. As these models continue to advance in scale and - sophistication, they will undoubtedly lead to further advancements in NLP and potentially even transform the way we - interact with technology. -

- -

- Introduction -

- -

- The field of natural language processing (NLP) has witnessed tremendous growth and advancements in recent years, with - the emergence of large language models being a significant contributor to this progress. These models have - revolutionized the way we approach NLP tasks, enabling us to perform complex linguistic operations with unprecedented - accuracy and efficiency. In this article, we will delve into the concept of large language models, their architectures, - applications, and the future prospects in this field. -

- -

- What are Large Language Models? -

- -

- Large language models are artificial intelligence (AI) models that are trained on vast amounts of text data to generate - language representations that can be used for various NLP tasks. These models are designed to learn the patterns and - structures of language, enabling them to generate coherent and contextually appropriate text. The key characteristic of - large language models is their scale, with some models containing billions of parameters and trillions of words in their - training datasets. -

- -

- Architectures of Large Language Models -

- -

- There are several types of large language models, each with its unique architecture and applications. Some of the most - popular architectures include: -

- -

- 1. Transformers: This architecture is based on a self-attention mechanism that allows the model to weigh the importance - of different words in a sequence. Transformers have achieved state-of-the-art results in various NLP tasks, including - language translation and text generation. -

-

- 2. Recurrent Neural Networks (RNNs): RNNs are designed to process sequences of data, making them well-suited for tasks - such as language modeling, machine translation, and text summarization. These models use loops to feed information from - one time step to the next, enabling them to capture temporal dependencies in language. -

-

- 3. Generative Adversarial Networks (GANs): GANs consist of two components: a generator network that generates text, and - a discriminator network that evaluates the generated text and provides feedback to the generator. This adversarial - process enables the generator to produce more realistic and coherent text over time. -

- -

- Applications of Large Language Models -

- -

- Large language models have numerous applications in NLP, including: -

- -

- 1. Language Translation: Large language models can be trained on parallel corpora of source and target languages to - learn the patterns and structures of both languages. This enables them to generate high-quality translations that are - contextually appropriate. -

-

- 2. Text Summarization: These models can be used to summarize long documents or articles, extracting the most important - information and generating a concise summary. -

-

- 3. Chatbots and Conversational Systems: Large language models can be used to generate responses to user queries in - chatbots and conversational systems, enabling them to understand and respond to complex questions and requests. -

-

- 4. Language Generation: These models can be used to generate coherent and contextually appropriate text, such as - articles, stories, or even entire books. -

- -

- Future Prospects of Large Language Models -

- -

- The future prospects of large language models are bright and promising. As these models continue to advance in scale and - sophistication, they will enable us to perform a wide range of NLP tasks with unprecedented accuracy and efficiency. - Some of the potential applications and advancements include: -

- -

- 1. Improved Language Translation: With the ability to learn multiple languages simultaneously, large language models - could potentially become multilingual, enabling them to translate between different language pairs more effectively. -

-

- 2. Enhanced Conversational Systems: As these models improve their ability to understand and respond to complex questions - and requests, they will become increasingly adept at engaging in natural-sounding conversations with humans. -

-

- 3. Increased Efficiency in NLP Tasks: Large language models have the potential to automate many NLP tasks, such as text - summarization, sentiment analysis, and question answering, freeing up human resources for more complex and creative - tasks. -

-

- 4. Advancements in Creative Writing: Large language models could potentially be used to generate original and coherent - creative writing, such as poetry, short stories, or even entire novels. -

- -

- Conclusion -

- -

- Large language models have revolutionized the field of NLP, enabling us to perform complex linguistic operations with - unprecedented accuracy and efficiency. These models have numerous applications in language translation, text - summarization, chatbots, and creative writing, among others. As these models continue to advance in scale and - sophistication, they will undoubtedly lead to further advancements in NLP and potentially even transform the way we - interact with technology. -

- -

- Introduction -

- -

- The field of natural language processing (NLP) has witnessed tremendous growth and advancements in recent years, with - the emergence of large language models being a significant contributor to this progress. These models have - revolutionized the way we approach NLP tasks, enabling us to perform complex linguistic operations with unprecedented - accuracy and efficiency. In this article, we will delve into the concept of large language models, their architectures, - applications, and the future prospects in this field. -

- -

- What are Large Language Models? -

- -

- Large language models are artificial intelligence (AI) models that are trained on vast amounts of text data to generate - language representations that can be used for various NLP tasks. These models are designed to learn the patterns and - structures of language, enabling them to generate coherent and contextually appropriate text. The key characteristic of - large language models is their scale, with some models containing billions of parameters and trillions of words in their - training datasets. -

- -

- Architectures of Large Language Models -

- -

- There are several types of large language models, each with its unique architecture and applications. Some of the most - popular architectures include: -

- -

- 1. Transformers: This architecture is based on a self-attention mechanism that allows the model to weigh the importance - of different words in a sequence. Transformers have achieved state-of-the-art results in various NLP tasks, including - language translation and text generation. -

-

- 2. Recurrent Neural Networks (RNNs): RNNs are designed to process sequences of data, making them well-suited for tasks - such as language modeling, machine translation, and text summarization. These models use loops to feed information from - one time step to the next, enabling them to capture temporal dependencies in language. -

-

- 3. Generative Adversarial Networks (GANs): GANs consist of two components: a generator network that generates text, and - a discriminator network that evaluates the generated text and provides feedback to the generator. This adversarial - process enables the generator to produce more realistic and coherent text over time. -

- -

- Applications of Large Language Models -

- -

- Large language models have numerous applications in NLP, including: -

- -

- 1. Language Translation: Large language models can be trained on parallel corpora of source and target languages to - learn the patterns and structures of both languages. This enables them to generate high-quality translations that are - contextually appropriate. -

-

- 2. Text Summarization: These models can be used to summarize long documents or articles, extracting the most important - information and generating a concise summary. -

-

- 3. Chatbots and Conversational Systems: Large language models can be used to generate responses to user queries in - chatbots and conversational systems, enabling them to understand and respond to complex questions and requests. -

-

- 4. Language Generation: These models can be used to generate coherent and contextually appropriate text, such as - articles, stories, or even entire books. -

- -

- Future Prospects of Large Language Models -

- -

- The future prospects of large language models are bright and promising. As these models continue to advance in scale and - sophistication, they will enable us to perform a wide range of NLP tasks with unprecedented accuracy and efficiency. - Some of the potential applications and advancements include: -

- -

- 1. Improved Language Translation: With the ability to learn multiple languages simultaneously, large language models - could potentially become multilingual, enabling them to translate between different language pairs more effectively. -

-

- 2. Enhanced Conversational Systems: As these models improve their ability to understand and respond to complex questions - and requests, they will become increasingly adept at engaging in natural-sounding conversations with humans. -

-

- 3. Increased Efficiency in NLP Tasks: Large language models have the potential to automate many NLP tasks, such as text - summarization, sentiment analysis, and question answering, freeing up human resources for more complex and creative - tasks. -

-

- 4. Advancements in Creative Writing: Large language models could potentially be used to generate original and coherent - creative writing, such as poetry, short stories, or even entire novels. -

- -

- Conclusion -

- -

- Large language models have revolutionized the field of NLP, enabling us to perform complex linguistic operations with - unprecedented accuracy and efficiency. These models have numerous applications in language translation, text - summarization, chatbots, and creative writing, among others. As these models continue to advance in scale and - sophistication, they will undoubtedly lead to further advancements in NLP and potentially even transform the way we - interact with technology. -

- -

- Introduction -

- -

- The field of natural language processing (NLP) has witnessed tremendous growth and advancements in recent years, with - the emergence of large language models being a significant contributor to this progress. These models have - revolutionized the way we approach NLP tasks, enabling us to perform complex linguistic operations with unprecedented - accuracy and efficiency. In this article, we will delve into the concept of large language models, their architectures, - applications, and the future prospects in this field. -

- -

- What are Large Language Models? -

- -

- Large language models are artificial intelligence (AI) models that are trained on vast amounts of text data to generate - language representations that can be used for various NLP tasks. These models are designed to learn the patterns and - structures of language, enabling them to generate coherent and contextually appropriate text. The key characteristic of - large language models is their scale, with some models containing billions of parameters and trillions of words in their - training datasets. -

- -

- Architectures of Large Language Models -

- -

- There are several types of large language models, each with its unique architecture and applications. Some of the most - popular architectures include: -

- -

- 1. Transformers: This architecture is based on a self-attention mechanism that allows the model to weigh the importance - of different words in a sequence. Transformers have achieved state-of-the-art results in various NLP tasks, including - language translation and text generation. -

-

- 2. Recurrent Neural Networks (RNNs): RNNs are designed to process sequences of data, making them well-suited for tasks - such as language modeling, machine translation, and text summarization. These models use loops to feed information from - one time step to the next, enabling them to capture temporal dependencies in language. -

-

- 3. Generative Adversarial Networks (GANs): GANs consist of two components: a generator network that generates text, and - a discriminator network that evaluates the generated text and provides feedback to the generator. This adversarial - process enables the generator to produce more realistic and coherent text over time. -

- -

- Applications of Large Language Models -

- -

- Large language models have numerous applications in NLP, including: -

- -

- 1. Language Translation: Large language models can be trained on parallel corpora of source and target languages to - learn the patterns and structures of both languages. This enables them to generate high-quality translations that are - contextually appropriate. -

-

- 2. Text Summarization: These models can be used to summarize long documents or articles, extracting the most important - information and generating a concise summary. -

-

- 3. Chatbots and Conversational Systems: Large language models can be used to generate responses to user queries in - chatbots and conversational systems, enabling them to understand and respond to complex questions and requests. -

-

- 4. Language Generation: These models can be used to generate coherent and contextually appropriate text, such as - articles, stories, or even entire books. -

- -

- Future Prospects of Large Language Models -

- -

- The future prospects of large language models are bright and promising. As these models continue to advance in scale and - sophistication, they will enable us to perform a wide range of NLP tasks with unprecedented accuracy and efficiency. - Some of the potential applications and advancements include: -

- -

- 1. Improved Language Translation: With the ability to learn multiple languages simultaneously, large language models - could potentially become multilingual, enabling them to translate between different language pairs more effectively. -

-

- 2. Enhanced Conversational Systems: As these models improve their ability to understand and respond to complex questions - and requests, they will become increasingly adept at engaging in natural-sounding conversations with humans. -

-

- 3. Increased Efficiency in NLP Tasks: Large language models have the potential to automate many NLP tasks, such as text - summarization, sentiment analysis, and question answering, freeing up human resources for more complex and creative - tasks. -

-

- 4. Advancements in Creative Writing: Large language models could potentially be used to generate original and coherent - creative writing, such as poetry, short stories, or even entire novels. -

- -

- Conclusion -

- -

- Large language models have revolutionized the field of NLP, enabling us to perform complex linguistic operations with - unprecedented accuracy and efficiency. These models have numerous applications in language translation, text - summarization, chatbots, and creative writing, among others. As these models continue to advance in scale and - sophistication, they will undoubtedly lead to further advancements in NLP and potentially even transform the way we - interact with technology. -

- - - \ No newline at end of file diff --git a/benchmark/visualization/all-http-latency-plot.sh b/benchmark/visualization/all-http-latency-plot.sh deleted file mode 100755 index 4ce6b69..0000000 --- a/benchmark/visualization/all-http-latency-plot.sh +++ /dev/null @@ -1,5 +0,0 @@ -./latency-plot.sh -m 40000 -o all-http-latency.png http-result-4c-monolake-tiny.txt http-result-4c-monolake-small.txt http-result-4c-monolake-medium.txt http-result-4c-monolake-large.txt http-result-4c-nginx-tiny.txt http-result-4c-nginx-small.txt http-result-4c-nginx-medium.txt http-result-4c-nginx-large.txt http-result-4c-traefik-tiny.txt http-result-4c-traefik-small.txt http-result-4c-traefik-medium.txt http-result-4c-traefik-large.txt http-result-4c-envoy-tiny.txt http-result-4c-envoy-small.txt http-result-4c-envoy-medium.txt http-result-4c-envoy-large.txt -./latency-plot.sh -m 40000 -o all-http-tiny-latency.png http-result-4c-monolake-tiny.txt http-result-4c-nginx-tiny.txt http-result-4c-traefik-tiny.txt http-result-4c-envoy-tiny.txt -./latency-plot.sh -m 40000 -o all-http-small-latency.png http-result-4c-monolake-small.txt http-result-4c-nginx-small.txt http-result-4c-traefik-small.txt http-result-4c-envoy-small.txt -./latency-plot.sh -m 40000 -o all-http-medium-latency.png http-result-4c-monolake-medium.txt http-result-4c-nginx-medium.txt http-result-4c-traefik-medium.txt http-result-4c-envoy-medium.txt -./latency-plot.sh -m 40000 -o all-http-large-latency.png http-result-4c-monolake-large.txt http-result-4c-nginx-large.txt http-result-4c-traefik-large.txt http-result-4c-envoy-large.txt diff --git a/benchmark/visualization/all-https-latency-plot.sh b/benchmark/visualization/all-https-latency-plot.sh deleted file mode 100755 index d1a4e51..0000000 --- a/benchmark/visualization/all-https-latency-plot.sh +++ /dev/null @@ -1,5 +0,0 @@ -./latency-plot.sh -m 40000 -o all-latency-https-tiny.png https-result-4c-monolake-tiny.txt https-result-4c-nginx-tiny.txt https-result-4c-traefik-tiny.txt https-result-4c-envoy-tiny.txt -./latency-plot.sh -m 40000 -o all-latency-https-small.png https-result-4c-monolake-small.txt https-result-4c-nginx-small.txt https-result-4c-traefik-small.txt https-result-4c-envoy-small.txt -./latency-plot.sh -m 40000 -o all-latency-https-medium.png https-result-4c-monolake-medium.txt https-result-4c-nginx-medium.txt https-result-4c-traefik-medium.txt https-result-4c-envoy-medium.txt -./latency-plot.sh -m 40000 -o all-latency-https-large.png https-result-4c-monolake-large.txt https-result-4c-nginx-large.txt https-result-4c-traefik-large.txt https-result-4c-envoy-large.txt -./latency-plot.sh -m 40000 -o all-latency-https.png https-result-4c-monolake-tiny.txt https-result-4c-monolake-small.txt https-result-4c-monolake-medium.txt https-result-4c-monolake-large.txt https-result-4c-nginx-tiny.txt https-result-4c-nginx-small.txt https-result-4c-nginx-medium.txt https-result-4c-nginx-large.txt https-result-4c-traefik-tiny.txt https-result-4c-traefik-small.txt https-result-4c-traefik-medium.txt https-result-4c-traefik-large.txt https-result-4c-envoy-tiny.txt https-result-4c-envoy-small.txt https-result-4c-envoy-medium.txt https-result-4c-envoy-large.txt diff --git a/benchmark/visualization/envoy-http-latency-plot.sh b/benchmark/visualization/envoy-http-latency-plot.sh deleted file mode 100755 index 34f352c..0000000 --- a/benchmark/visualization/envoy-http-latency-plot.sh +++ /dev/null @@ -1 +0,0 @@ -./latency-plot.sh -m 40000 -o envoy-http-latency.png http-result-4c-envoy-tiny.txt http-result-4c-envoy-small.txt http-result-4c-envoy-medium.txt http-result-4c-envoy-large.txt diff --git a/benchmark/visualization/envoy-https-latency-plot.sh b/benchmark/visualization/envoy-https-latency-plot.sh deleted file mode 100755 index 9b695fe..0000000 --- a/benchmark/visualization/envoy-https-latency-plot.sh +++ /dev/null @@ -1 +0,0 @@ -./latency-plot.sh -m 40000 -o envoy-https-latency.png https-result-4c-envoy-tiny.txt https-result-4c-envoy-small.txt https-result-4c-envoy-medium.txt https-result-4c-envoy-large.txt diff --git a/benchmark/visualization/latency-plot.sh b/benchmark/visualization/latency-plot.sh deleted file mode 100755 index 2453e89..0000000 --- a/benchmark/visualization/latency-plot.sh +++ /dev/null @@ -1,95 +0,0 @@ -#!/bin/sh -# -# * Written by Gil Tene of Azul Systems, and released to the public domain, -# * as explained at http://creativecommons.org/publicdomain/zero/1.0/ -# -# This script uses gnuplot to plot the percentile distribution in the -# input files provided. run with "-h" option for an expected usage description. -# -# The script assumes the input files contain ".hgrm" formatted output such -# as the one provided by HdrHistogram. The 4th column in the input files is -# expected to be the value of 1/(1-percentile) (for a given percentile), -# and the 1st column in the input files is expected to be the value at the -# given percentile. -# - -reading_SLA_NAME=0 -reading_OUTPUT_NAME=0 -helpFlagFound=0 -SLA_NAME= -FILES= -OUTPUT_FILENAME= -reading_maxvalue=0 -maxvalue= - -for var in $@; do - if [ $reading_SLA_NAME -eq 1 ]; then - SLA_NAME=$var - reading_SLA_NAME=0 - elif [ $reading_OUTPUT_NAME -eq 1 ]; then - OUTPUT_FILENAME=$var - reading_OUTPUT_NAME=0 - elif [ $reading_maxvalue -eq 1 ]; then - maxvalue="set yrange [0:$var]" - reading_maxvalue=0 - elif [ $var = "-h" ]; then - helpFlagFound=1 - elif [ $var = "-o" ]; then - reading_OUTPUT_NAME=1 - elif [ $var = "-s" ]; then - reading_SLA_NAME=1 - elif [ $var = "-m" ]; then - reading_maxvalue=1 - else - FILES="$FILES $var" - fi -done - -message() -{ - echo "$@" >&2 -} - -if [ $helpFlagFound -eq 1 ]; then - message "Usage: latency-plot.sh [-o output_file] [-s sla_file] histogram_file ..." - exit 255 -fi - -echo "1.0 0.0 0%" > ./xlabels.dat -echo "10.0 0.0 90%" >> ./xlabels.dat -echo "100.0 0.0 99%" >> ./xlabels.dat -echo "1000.0 0.0 99.9%" >> ./xlabels.dat -echo "10000.0 0.0 99.99%" >> ./xlabels.dat -echo "100000.0 0.0 99.999%" >> ./xlabels.dat -echo "1000000.0 0.0 99.9999%" >> ./xlabels.dat - -IndividualFilePlotCommands="'./xlabels.dat' with labels center offset 0, 1.5 point" -for file in $FILES; do - IndividualFilePlotCommands="$IndividualFilePlotCommands, '$file' using 4:1 with lines" -done - -if [ $SLA_NAME ]; then - IndividualFilePlotCommands="$IndividualFilePlotCommands, '$SLA_NAME' with lines ls 1" - message plotting "{ " $FILES " }" with SLA $SLA_NAME -else - message plotting "{ " $FILES " }" -fi - -message command will be: -message $IndividualFilePlotCommands - -( - echo "#plot commands" - echo "set terminal png size 1280,720" - if [ $OUTPUT_FILENAME ]; then - echo "set output '$OUTPUT_FILENAME'" - fi - echo "set logscale x" - echo "unset xtics" - echo "$maxvalue" - echo "set key top left" - echo "set style line 1 lt 1 lw 3 pt 3 linecolor rgb \"red\"" - echo "plot $IndividualFilePlotCommands" - echo "set yr [GPVAL_DATA_Y_MIN:GPVAL_DATA_Y_MAX]" - echo "replot" -) | gnuplot diff --git a/benchmark/visualization/monolake-http-latency-plot.sh b/benchmark/visualization/monolake-http-latency-plot.sh deleted file mode 100755 index 5a6c16d..0000000 --- a/benchmark/visualization/monolake-http-latency-plot.sh +++ /dev/null @@ -1 +0,0 @@ -./latency-plot.sh -m 40000 -o monolake-http-latency.png http-result-4c-monolake-tiny.txt http-result-4c-monolake-small.txt http-result-4c-monolake-medium.txt http-result-4c-monolake-large.txt diff --git a/benchmark/visualization/monolake-https-latency-plot.sh b/benchmark/visualization/monolake-https-latency-plot.sh deleted file mode 100755 index 3f59199..0000000 --- a/benchmark/visualization/monolake-https-latency-plot.sh +++ /dev/null @@ -1 +0,0 @@ -./latency-plot.sh -m 40000 -o monolake-https-latency.png https-result-4c-monolake-tiny.txt https-result-4c-monolake-small.txt https-result-4c-monolake-medium.txt https-result-4c-monolake-large.txt diff --git a/benchmark/visualization/nginx-http-latency-plot.sh b/benchmark/visualization/nginx-http-latency-plot.sh deleted file mode 100755 index b3e227c..0000000 --- a/benchmark/visualization/nginx-http-latency-plot.sh +++ /dev/null @@ -1 +0,0 @@ -./latency-plot.sh -m 40000 -o nginx-http-latency.png http-result-4c-nginx-tiny.txt http-result-4c-nginx-small.txt http-result-4c-nginx-medium.txt http-result-4c-nginx-large.txt diff --git a/benchmark/visualization/nginx-https-latency-plot.sh b/benchmark/visualization/nginx-https-latency-plot.sh deleted file mode 100755 index 8ff3ddf..0000000 --- a/benchmark/visualization/nginx-https-latency-plot.sh +++ /dev/null @@ -1 +0,0 @@ -./latency-plot.sh -m 40000 -o nginx-https-latency.png https-result-4c-nginx-tiny.txt https-result-4c-nginx-small.txt https-result-4c-nginx-medium.txt https-result-4c-nginx-large.txt diff --git a/benchmark/visualization/performance-csv-convert.py b/benchmark/visualization/performance-csv-convert.py deleted file mode 100644 index 3b4e0fe..0000000 --- a/benchmark/visualization/performance-csv-convert.py +++ /dev/null @@ -1,130 +0,0 @@ -import csv - -in_filed_name = ['Case', 'Requests/sec', 'Transfer 10K/sec', 'Server Error', 'Timeout'] - -csv_filename = "proxies-performance.csv" -with open(csv_filename, 'r') as csvfile: - reader = csv.reader(csvfile, delimiter=',') - next(reader) - d_csv = dict() - for j, row in enumerate(reader): - d_row = dict() - for i, column in enumerate(row): - if i == 0: - continue - d_row[i-1] = column - d_csv[j] = d_row - -# print("d_csv:") -# print(d_csv) - -fieldnames = ["Case", - "Tiny Requests/sec", "Small Requests/sec", "Medium Requests/sec", "Large Requests/sec", - "Tiny Transfer/sec", "Small Transfer/sec", "Medium Transfer/sec", "Large Transfer/sec", ] - -o_csv = dict() -o_csv["http-monolake"] = list() -o_csv["http-nginx"] = list() -o_csv["http-traefik"] = list() -o_csv["http-envoy"] = list() -o_csv["https-monolake"] = list() -o_csv["https-nginx"] = list() -o_csv["https-traefik"] = list() -o_csv["https-envoy"] = list() - -o_csv["http-monolake"].append("http-monolake") -o_csv["http-monolake"].append(d_csv[0][0]) -o_csv["http-monolake"].append(d_csv[1][0]) -o_csv["http-monolake"].append(d_csv[2][0]) -o_csv["http-monolake"].append(d_csv[3][0]) -o_csv["http-monolake"].append(d_csv[0][1]) -o_csv["http-monolake"].append(d_csv[1][1]) -o_csv["http-monolake"].append(d_csv[2][1]) -o_csv["http-monolake"].append(d_csv[3][1]) - -o_csv["http-nginx"].append("http-nginx") -o_csv["http-nginx"].append(d_csv[4][0]) -o_csv["http-nginx"].append(d_csv[5][0]) -o_csv["http-nginx"].append(d_csv[6][0]) -o_csv["http-nginx"].append(d_csv[7][0]) -o_csv["http-nginx"].append(d_csv[4][1]) -o_csv["http-nginx"].append(d_csv[5][1]) -o_csv["http-nginx"].append(d_csv[6][1]) -o_csv["http-nginx"].append(d_csv[7][1]) - -o_csv["http-traefik"].append("http-traefik") -o_csv["http-traefik"].append(d_csv[8][0]) -o_csv["http-traefik"].append(d_csv[9][0]) -o_csv["http-traefik"].append(d_csv[10][0]) -o_csv["http-traefik"].append(d_csv[11][0]) -o_csv["http-traefik"].append(d_csv[8][1]) -o_csv["http-traefik"].append(d_csv[9][1]) -o_csv["http-traefik"].append(d_csv[10][1]) -o_csv["http-traefik"].append(d_csv[11][1]) - -o_csv["http-envoy"].append("http-envoy") -o_csv["http-envoy"].append(d_csv[12][0]) -o_csv["http-envoy"].append(d_csv[13][0]) -o_csv["http-envoy"].append(d_csv[14][0]) -o_csv["http-envoy"].append(d_csv[15][0]) -o_csv["http-envoy"].append(d_csv[12][1]) -o_csv["http-envoy"].append(d_csv[13][1]) -o_csv["http-envoy"].append(d_csv[14][1]) -o_csv["http-envoy"].append(d_csv[15][1]) - -o_csv["https-monolake"].append("https-monolake") -o_csv["https-monolake"].append(d_csv[16][0]) -o_csv["https-monolake"].append(d_csv[17][0]) -o_csv["https-monolake"].append(d_csv[18][0]) -o_csv["https-monolake"].append(d_csv[19][0]) -o_csv["https-monolake"].append(d_csv[16][1]) -o_csv["https-monolake"].append(d_csv[17][1]) -o_csv["https-monolake"].append(d_csv[18][1]) -o_csv["https-monolake"].append(d_csv[19][1]) - -o_csv["https-nginx"].append("https-nginx") -o_csv["https-nginx"].append(d_csv[20][0]) -o_csv["https-nginx"].append(d_csv[21][0]) -o_csv["https-nginx"].append(d_csv[22][0]) -o_csv["https-nginx"].append(d_csv[23][0]) -o_csv["https-nginx"].append(d_csv[20][1]) -o_csv["https-nginx"].append(d_csv[21][1]) -o_csv["https-nginx"].append(d_csv[22][1]) -o_csv["https-nginx"].append(d_csv[23][1]) - -o_csv["https-traefik"].append("https-traefik") -o_csv["https-traefik"].append(d_csv[24][0]) -o_csv["https-traefik"].append(d_csv[25][0]) -o_csv["https-traefik"].append(d_csv[26][0]) -o_csv["https-traefik"].append(d_csv[27][0]) -o_csv["https-traefik"].append(d_csv[24][1]) -o_csv["https-traefik"].append(d_csv[25][1]) -o_csv["https-traefik"].append(d_csv[26][1]) -o_csv["https-traefik"].append(d_csv[27][1]) - -o_csv["https-envoy"].append("https-envoy") -o_csv["https-envoy"].append(d_csv[28][0]) -o_csv["https-envoy"].append(d_csv[29][0]) -o_csv["https-envoy"].append(d_csv[30][0]) -o_csv["https-envoy"].append(d_csv[31][0]) -o_csv["https-envoy"].append(d_csv[28][1]) -o_csv["https-envoy"].append(d_csv[29][1]) -o_csv["https-envoy"].append(d_csv[30][1]) -o_csv["https-envoy"].append(d_csv[31][1]) - -# print("o_csv:") -# print(o_csv) - -output_filename1 = "proxies-performance-rotated.csv" - -with open(output_filename1, 'w') as csvfile_output: - writer = csv.writer(csvfile_output, delimiter=',') - writer.writerow(fieldnames) - writer.writerow(o_csv["http-monolake"]) - writer.writerow(o_csv["http-nginx"]) - writer.writerow(o_csv["http-traefik"]) - writer.writerow(o_csv["http-envoy"]) - writer.writerow(o_csv["https-monolake"]) - writer.writerow(o_csv["https-nginx"]) - writer.writerow(o_csv["https-traefik"]) - writer.writerow(o_csv["https-envoy"]) diff --git a/benchmark/visualization/performance-plot.sh b/benchmark/visualization/performance-plot.sh deleted file mode 100755 index 1ada155..0000000 --- a/benchmark/visualization/performance-plot.sh +++ /dev/null @@ -1,94 +0,0 @@ -#!/bin/bash - -# process to monitor -process_name=$1 -dir_name="." -csv_filename="${1}-performance.csv" - -# Read collected metrices from the CSV file and plot graphs -# -# This function will end script execution. -# -# This function is to be called after an interrupt like SIGINT or SIGKILL -# is received. -# -function plotGraph() { - - # bring cursor to next line after interrupt - echo - - # plot graphs if there is a data file - if [ -f $csv_filename ]; then - echo "Plotting graphs..." - gnuplot <<- EOF - # Output to png with a font size of 10, using pngcairo for anti-aliasing - set term pngcairo size 1024,800 noenhanced font "Helvetica,10" - - # Set border color around the graph - set border ls 50 lt rgb "#939393" - - # Hide left and right vertical borders - set border 16 lw 0 - set border 64 lw 0 - - # Set tic color - set tics nomirror textcolor rgb "#939393" - - # Set horizontal lines on the ytics - set grid ytics lt 1 lc rgb "#d8d8d8" lw 2 - - # Rotate x axis lables - set xtics rotate - - # Set graph size relative to the canvas - set size 1,0.85 - - # Set separator to comma - set datafile separator "," - - # Move legend to the bottom - set key bmargin center box lt rgb "#d8d8d8" horizontal - - # Plot graph, - # xticlabels(1) - first column as x tic labels - # "with lines" - line graph - # "smooth unique" - # "lw 2" - line width - # "lt rgb " - line style color - # "t " - legend labels - # - # CPU and memory usage - set output "${dir_name}/cpu-mem-usage-${process_name}.png" - set title "CPU and Memory Usage for Proces ${process_name}" - plot "$csv_filename" using 2:xticlabels(1) with lines smooth unique lw 2 lt rgb "#4848d6" t "CPU Usage %",\ - "$csv_filename" using 3:xticlabels(1) with lines smooth unique lw 2 lt rgb "#b40000" t "Memory Usage %" - - # TCP count - set output "${dir_name}/tcp-count-${process_name}.png" - set title "TCP Connections Count for Proces ${process_name}" - plot "$csv_filename" using 4:xticlabels(1) with lines smooth unique lw 2 lt rgb "#ed8004" t "TCP Connection Count" - - # Thread count - set output "${dir_name}/thread-count-${process_name}.png" - set title "Thread Count for Proces ${process_name}" - plot "$csv_filename" using 5:xticlabels(1) with lines smooth unique lw 2 lt rgb "#48d65b" t "Thread Count" - - # All together - set output "${dir_name}/performance-metrices-${process_name}.png" - set title "Performance Metrics for Proces ${process_name}" - plot "$csv_filename" using 2:xticlabels(1) with lines smooth unique lw 2 lt rgb "#4848d6" t "CPU Usage %",\ - "$csv_filename" using 3:xticlabels(1) with lines smooth unique lw 2 lt rgb "#b40000" t "Memory Usage %", \ - "$csv_filename" using 4:xticlabels(1) with lines smooth unique lw 2 lt rgb "#ed8004" t "TCP Connection Count", \ - "$csv_filename" using 5:xticlabels(1) with lines smooth unique lw 2 lt rgb "#48d65b" t "Thread Count" -EOF - fi - - echo "Done!" - exit 0 -} - -# add SIGINT & SIGTERM trap -trap "plotGraph" SIGINT SIGTERM SIGKILL - -# draw graph -plotGraph diff --git a/benchmark/visualization/proxies-performance-plot.sh b/benchmark/visualization/proxies-performance-plot.sh deleted file mode 100755 index 5ad52ea..0000000 --- a/benchmark/visualization/proxies-performance-plot.sh +++ /dev/null @@ -1,598 +0,0 @@ -FILES="http-result-4c-monolake-tiny.txt http-result-4c-monolake-small.txt http-result-4c-monolake-medium.txt http-result-4c-monolake-large.txt http-result-4c-nginx-tiny.txt http-result-4c-nginx-small.txt http-result-4c-nginx-medium.txt http-result-4c-nginx-large.txt http-result-4c-traefik-tiny.txt http-result-4c-traefik-small.txt http-result-4c-traefik-medium.txt http-result-4c-traefik-large.txt http-result-4c-envoy-tiny.txt http-result-4c-envoy-small.txt http-result-4c-envoy-medium.txt http-result-4c-envoy-large.txt https-result-4c-monolake-tiny.txt https-result-4c-monolake-small.txt https-result-4c-monolake-medium.txt https-result-4c-monolake-large.txt https-result-4c-nginx-tiny.txt https-result-4c-nginx-small.txt https-result-4c-nginx-medium.txt https-result-4c-nginx-large.txt https-result-4c-traefik-tiny.txt https-result-4c-traefik-small.txt https-result-4c-traefik-medium.txt https-result-4c-traefik-large.txt https-result-4c-envoy-tiny.txt https-result-4c-envoy-small.txt https-result-4c-envoy-medium.txt https-result-4c-envoy-large.txt" -csv_filename="proxies-performance.csv" -# output_filename0="proxies-performance-boxes.png" -output_filename1="proxies-performance.png" -csv_filename2="proxies-performance-rotated.csv" -output_filename2="proxies-performance-rotated.png" -output_filename_tiny_throughput="tiny-throughput.png" -output_filename_small_throughput="small-throughput.png" -output_filename_medium_throughput="medium-throughput.png" -output_filename_large_throughput="large-throughput.png" -output_filename_tiny_qps="tiny-qps.png" -output_filename_small_qps="small-qps.png" -output_filename_medium_qps="medium-qps.png" -output_filename_large_qps="large-qps.png" -# output_filename3="proxies-performance-rotated-boxes.png" - -echo "Case,Requests/sec,Transfer/sec,Server Error,Timeout" > $csv_filename - -for f in $FILES -do - echo "Processing $f file..." - Line=$( tail -n 1 $f ) - Transfer=${Line##* } - if [[ $Transfer == *"MB" ]]; then - Bytes=${Transfer:0:${#Transfer} - 2} - Bytes=$(echo "$Bytes * 1024 * 1024" | bc) - # Bytes=$(echo "$Bytes * 100" | bc) - elif [[ $Transfer == *"GB" ]]; then - Bytes=${Transfer:0:${#Transfer} - 2} - Bytes=$(echo "$Bytes * 1024 * 1024 *1024" | bc) - # Bytes=$(echo "$Bytes * 102400" | bc) - else - Bytes=${Transfer:0:${#Transfer} - 2} - Bytes=$(echo "$Bytes * 1024" | bc) - # Bytes=$(echo "$Bytes / 10.24" | bc) - fi - Line=$( tail -n 2 $f | head -n 1 ) - Request=${Line##* } - Line=$( tail -n 3 $f | head -n 1 ) - if [[ $Line == *"Non-2xx"* ]]; then - ServerError=${Line##* } - Line=$( tail -n 4 $f | head -n 1 ) - else - ServerError="0" - fi - if [[ $Line == *"Socket errors"* ]]; then - Timeout=${Line##* } - else - Timeout="0" - fi - Case=`echo "$f" | cut -d'.' -f1` - echo "$Case,$Request,$Bytes,$ServerError,$Timeout" >> $csv_filename -done - -python3 performance-csv-convert.py - -echo "Plotting graphs..." -gnuplot <<- EOF - # Output to png with a font size of 10, using pngcairo for anti-aliasing - set term pngcairo size 1024,800 noenhanced font "Helvetica,10" - - # Set border color around the graph - set border ls 50 lt rgb "#939393" - - # Hide left and right vertical borders - set border 16 lw 0 - set border 64 lw 0 - - # Set tic color - set tics nomirror textcolor rgb "#939393" - - # Set horizontal lines on the ytics - set grid ytics lt 1 lc rgb "#d8d8d8" lw 2 - - # Rotate x axis lables - set xtics rotate - - # Set graph size relative to the canvas - set size 1,0.85 - - # Set separator to comma - set datafile separator "," - - # Move legend to the bottom - set key bmargin center box lt rgb "#d8d8d8" horizontal - - # Plot graph, - # xticlabels(1) - first column as x tic labels - # "with lines" - line graph - # "smooth unique" - # "lw 2" - line width - # "lt rgb " - line style color - # "t " - legend labels - # - # Proxy Services Performance - # set output "$output_filename1" - # set title "Proxy Services Performance" - # plot "$csv_filename" using 2:xticlabels(1) with lines smooth unique lw 2 lt rgb "#4848d6" t "Requests/sec",\ - # "$csv_filename" using 3:xticlabels(1) with lines smooth unique lw 2 lt rgb "#b40000" t "Transfer 10KB/sec", \ - # "$csv_filename" using 4:xticlabels(1) with lines smooth unique lw 2 lt rgb "#ed8004" t "Server Error", \ - # "$csv_filename" using 5:xticlabels(1) with lines smooth unique lw 2 lt rgb "#48d65b" t "Timeout", - - # set output "$output_filename0" - set output "$output_filename1" - set yrange [1000:*] - set logscale y - set ytics format "%.1s%c" - - set style data histogram - set style histogram cluster gap 1 - set style fill solid border -1 - set boxwidth 1 - - plot "$csv_filename" u 3:xtic(1) ti col,\ - '' u 2 ti col, - # '' u 4 ti col,\ - # '' u 5 ti col, -EOF - -echo "Plotting graphs rotated..." -gnuplot <<- EOF - # Output to png with a font size of 10, using pngcairo for anti-aliasing - set term pngcairo size 1024,800 noenhanced font "Helvetica,10" - - # Set border color around the graph - set border ls 50 lt rgb "#939393" - - # Hide left and right vertical borders - set border 16 lw 0 - set border 64 lw 0 - - # Set tic color - set tics nomirror textcolor rgb "#939393" - - # Set horizontal lines on the ytics - set grid ytics lt 1 lc rgb "#d8d8d8" lw 2 - - # Rotate x axis lables - # set xtics rotate - - # Set graph size relative to the canvas - set size 1,0.85 - - set boxwidth 0.5 - set style fill solid - - # Set separator to comma - set datafile separator "," - - # Move legend to the bottom - set key bmargin center box lt rgb "#d8d8d8" horizontal - - # Plot graph, - # xticlabels(1) - first column as x tic labels - # "with lines" - line graph - # "smooth unique" - # "lw 2" - line width - # "lt rgb " - line style color - # "t " - legend labels - # - # Proxy Services Performance - # set output "$output_filename2" - # set title "Proxy Services Performance By Payload" - - # plot "$csv_filename2" using 2:xticlabels(1) with lines smooth unique lw 2 lt rgb "#ff0000" t "Tiny Requests/sec",\ - # "$csv_filename2" using 3:xticlabels(1) with lines smooth unique lw 2 lt rgb "#00ff00" t "Small Requests/sec", \ - # "$csv_filename2" using 4:xticlabels(1) with lines smooth unique lw 2 lt rgb "#0000ff" t "Medium Requests/sec", \ - # "$csv_filename2" using 5:xticlabels(1) with lines smooth unique lw 2 lt rgb "#000000" t "Large Requests/sec", \ - # "$csv_filename2" using 6:xticlabels(1) with lines smooth unique lw 2 lt rgb "#800000" t "Tiny Transfer 10KB/sec",\ - # "$csv_filename2" using 7:xticlabels(1) with lines smooth unique lw 2 lt rgb "#008000" t "Small Transfer 10KB/sec", \ - # "$csv_filename2" using 8:xticlabels(1) with lines smooth unique lw 2 lt rgb "#000080" t "Medium Transfer 10KB/sec", \ - # "$csv_filename2" using 9:xticlabels(1) with lines smooth unique lw 2 lt rgb "#808080" t "Large Transfer 10KB/sec", - - # set output "$output_filename3" - set output "$output_filename2" - set title "Proxy Services Performance By Proxy" - - set yrange [1000:*] - set logscale y - set ytics format "%.1s%c" - - set style data histogram - set style histogram cluster gap 1 - set style fill solid border -1 - set boxwidth 0.9 - - plot "$csv_filename2" u 6:xtic(1) ti col,\ - '' u 7 ti col,\ - '' u 8 ti col,\ - '' u 9 ti col,\ - '' u 2 ti col,\ - '' u 3 ti col,\ - '' u 4 ti col,\ - '' u 5 ti col, -EOF - -echo "Plotting itemized graphs rotated..." -gnuplot <<- EOF - # Output to png with a font size of 10, using pngcairo for anti-aliasing - set term pngcairo size 1024,800 noenhanced font "Helvetica,10" - - # Set border color around the graph - set border ls 50 lt rgb "#939393" - - # Hide left and right vertical borders - set border 16 lw 0 - set border 64 lw 0 - - # Set tic color - set tics nomirror textcolor rgb "#939393" - - # Set horizontal lines on the ytics - set grid ytics lt 1 lc rgb "#d8d8d8" lw 2 - - # Rotate x axis lables - # set xtics rotate - - # Set graph size relative to the canvas - set size 1,0.85 - - set boxwidth 0.5 - set style fill solid - - # Set separator to comma - set datafile separator "," - - # Move legend to the bottom - set key bmargin center box lt rgb "#d8d8d8" horizontal - - set output "$output_filename_tiny_throughput" - set title "Proxy Throughput of HTTP/S Tiny Payload" - - # set yrange [1000:*] - set ytics format "%.1s%c" - - set style data histogram - set style histogram cluster gap 1 - set style fill solid border -1 - set boxwidth 0.9 - - plot "$csv_filename2" using 6:xtic(1) ti '' with boxes fc rgb "#4848d6" -EOF - -gnuplot <<- EOF - # Output to png with a font size of 10, using pngcairo for anti-aliasing - set term pngcairo size 1024,800 noenhanced font "Helvetica,10" - - # Set border color around the graph - set border ls 50 lt rgb "#939393" - - # Hide left and right vertical borders - set border 16 lw 0 - set border 64 lw 0 - - # Set tic color - set tics nomirror textcolor rgb "#939393" - - # Set horizontal lines on the ytics - set grid ytics lt 1 lc rgb "#d8d8d8" lw 2 - - # Rotate x axis lables - # set xtics rotate - - # Set graph size relative to the canvas - set size 1,0.85 - - set boxwidth 0.5 - set style fill solid - - # Set separator to comma - set datafile separator "," - - # Move legend to the bottom - set key bmargin center box lt rgb "#d8d8d8" horizontal - - set output "$output_filename_small_throughput" - set title "Proxy Throughput of HTTP/S Small Payload" - - # set yrange [1000:*] - set ytics format "%.1s%c" - - set style data histogram - set style histogram cluster gap 1 - set style fill solid border -1 - set boxwidth 0.9 - - plot "$csv_filename2" using 7:xtic(1) ti '' with boxes fc rgb "#b40000" -EOF - -gnuplot <<- EOF - # Output to png with a font size of 10, using pngcairo for anti-aliasing - set term pngcairo size 1024,800 noenhanced font "Helvetica,10" - - # Set border color around the graph - set border ls 50 lt rgb "#939393" - - # Hide left and right vertical borders - set border 16 lw 0 - set border 64 lw 0 - - # Set tic color - set tics nomirror textcolor rgb "#939393" - - # Set horizontal lines on the ytics - set grid ytics lt 1 lc rgb "#d8d8d8" lw 2 - - # Rotate x axis lables - # set xtics rotate - - # Set graph size relative to the canvas - set size 1,0.85 - - set boxwidth 0.5 - set style fill solid - - # Set separator to comma - set datafile separator "," - - # Move legend to the bottom - set key bmargin center box lt rgb "#d8d8d8" horizontal - - set output "$output_filename_medium_throughput" - set title "Proxy Throughput of HTTP/S Medium Payload" - - # set yrange [1000:*] - set ytics format "%.1s%c" - - set style data histogram - set style histogram cluster gap 1 - set style fill solid border -1 - set boxwidth 0.9 - - plot "$csv_filename2" using 8:xtic(1) ti '' with boxes fc rgb "#ed8004" -EOF - -gnuplot <<- EOF - # Output to png with a font size of 10, using pngcairo for anti-aliasing - set term pngcairo size 1024,800 noenhanced font "Helvetica,10" - - # Set border color around the graph - set border ls 50 lt rgb "#939393" - - # Hide left and right vertical borders - set border 16 lw 0 - set border 64 lw 0 - - # Set tic color - set tics nomirror textcolor rgb "#939393" - - # Set horizontal lines on the ytics - set grid ytics lt 1 lc rgb "#d8d8d8" lw 2 - - # Rotate x axis lables - # set xtics rotate - - # Set graph size relative to the canvas - set size 1,0.85 - - set boxwidth 0.5 - set style fill solid - - # Set separator to comma - set datafile separator "," - - # Move legend to the bottom - set key bmargin center box lt rgb "#d8d8d8" horizontal - - set output "$output_filename_large_throughput" - set title "Proxy Throughput of HTTP/S Large Payload" - - # set yrange [1000:*] - set ytics format "%.1s%c" - - set style data histogram - set style histogram cluster gap 1 - set style fill solid border -1 - set boxwidth 0.9 - - plot "$csv_filename2" using 9:xtic(1) ti '' with boxes fc rgb "#48d65b" -EOF - -gnuplot <<- EOF - # Output to png with a font size of 10, using pngcairo for anti-aliasing - set term pngcairo size 1024,800 noenhanced font "Helvetica,10" - - # Set border color around the graph - set border ls 50 lt rgb "#939393" - - # Hide left and right vertical borders - set border 16 lw 0 - set border 64 lw 0 - - # Set tic color - set tics nomirror textcolor rgb "#939393" - - # Set horizontal lines on the ytics - set grid ytics lt 1 lc rgb "#d8d8d8" lw 2 - - # Rotate x axis lables - # set xtics rotate - - # Set graph size relative to the canvas - set size 1,0.85 - - set boxwidth 0.5 - set style fill solid - - # Set separator to comma - set datafile separator "," - - # Move legend to the bottom - set key bmargin center box lt rgb "#d8d8d8" horizontal - - set output "$output_filename_tiny_qps" - set title "Proxy QPS of HTTP/S Tiny Payload" - - # set yrange [1000:*] - set ytics format "%.1s%c" - - set style data histogram - set style histogram cluster gap 1 - set style fill solid border -1 - set boxwidth 0.9 - - plot "$csv_filename2" using 2:xtic(1) ti '' with boxes fc rgb "#4848d6" -EOF - -gnuplot <<- EOF - # Output to png with a font size of 10, using pngcairo for anti-aliasing - set term pngcairo size 1024,800 noenhanced font "Helvetica,10" - - # Set border color around the graph - set border ls 50 lt rgb "#939393" - - # Hide left and right vertical borders - set border 16 lw 0 - set border 64 lw 0 - - # Set tic color - set tics nomirror textcolor rgb "#939393" - - # Set horizontal lines on the ytics - set grid ytics lt 1 lc rgb "#d8d8d8" lw 2 - - # Rotate x axis lables - # set xtics rotate - - # Set graph size relative to the canvas - set size 1,0.85 - - set boxwidth 0.5 - set style fill solid - - # Set separator to comma - set datafile separator "," - - # Move legend to the bottom - set key bmargin center box lt rgb "#d8d8d8" horizontal - - set output "$output_filename_small_qps" - set title "Proxy QPS of HTTP/S Small Payload" - - # set yrange [1000:*] - set ytics format "%.1s%c" - - set style data histogram - set style histogram cluster gap 1 - set style fill solid border -1 - set boxwidth 0.9 - - plot "$csv_filename2" using 3:xtic(1) ti '' with boxes fc rgb "#b40000" -EOF - -gnuplot <<- EOF - # Output to png with a font size of 10, using pngcairo for anti-aliasing - set term pngcairo size 1024,800 noenhanced font "Helvetica,10" - - # Set border color around the graph - set border ls 50 lt rgb "#939393" - - # Hide left and right vertical borders - set border 16 lw 0 - set border 64 lw 0 - - # Set tic color - set tics nomirror textcolor rgb "#939393" - - # Set horizontal lines on the ytics - set grid ytics lt 1 lc rgb "#d8d8d8" lw 2 - - # Rotate x axis lables - # set xtics rotate - - # Set graph size relative to the canvas - set size 1,0.85 - - set boxwidth 0.5 - set style fill solid - - # Set separator to comma - set datafile separator "," - - # Move legend to the bottom - set key bmargin center box lt rgb "#d8d8d8" horizontal - - set output "$output_filename_medium_qps" - set title "Proxy QPS of HTTP/S Medium Payload" - - # set yrange [1000:*] - set ytics format "%.1s%c" - - set style data histogram - set style histogram cluster gap 1 - set style fill solid border -1 - set boxwidth 0.9 - - plot "$csv_filename2" using 4:xtic(1) ti '' with boxes fc rgb "#ed8004" -EOF - -gnuplot <<- EOF - # Output to png with a font size of 10, using pngcairo for anti-aliasing - set term pngcairo size 1024,800 noenhanced font "Helvetica,10" - - # Set border color around the graph - set border ls 50 lt rgb "#939393" - - # Hide left and right vertical borders - set border 16 lw 0 - set border 64 lw 0 - - # Set tic color - set tics nomirror textcolor rgb "#939393" - - # Set horizontal lines on the ytics - set grid ytics lt 1 lc rgb "#d8d8d8" lw 2 - - # Rotate x axis lables - # set xtics rotate - - # Set graph size relative to the canvas - set size 1,0.85 - - set boxwidth 0.5 - set style fill solid - - # Set separator to comma - set datafile separator "," - - # Move legend to the bottom - set key bmargin center box lt rgb "#d8d8d8" horizontal - - set output "$output_filename_large_qps" - set title "Proxy QPS of HTTP/S Large Payload" - - # set yrange [1000:*] - set ytics format "%.1s%c" - - set style data histogram - set style histogram cluster gap 1 - set style fill solid border -1 - set boxwidth 0.9 - - plot "$csv_filename2" using 5:xtic(1) ti '' with boxes fc rgb "#48d65b" -EOF - -echo "copy generated images to ../images/README" -cp performance-metrices-monolake.png ../images/README/ -# cp proxies-performance-rotated.png ../images/README/ -# cp proxies-performance.png ../images/README/ -cp nginx-http-latency.png ../images/README/ -cp all-http-latency.png ../images/README/ -cp all-latency-https.png ../images/README/ -# cp large-qps.png ../images/README/ -# cp medium-qps.png ../images/README/ -# cp small-qps.png ../images/README/ -# cp tiny-qps.png ../images/README/ -# cp large-throughput.png ../images/README/ -# cp medium-throughput.png ../images/README/ -# cp small-throughput.png ../images/README/ -# cp tiny-throughput.png ../images/README/ -cp all-latency-https-large.png ../images/README/ -cp all-latency-https-medium.png ../images/README/ -cp all-latency-https-small.png ../images/README/ -cp all-latency-https-tiny.png ../images/README/ -cp all-http-large-latency.png ../images/README/ -cp all-http-medium-latency.png ../images/README/ -cp all-http-small-latency.png ../images/README/ -cp all-http-tiny-latency.png ../images/README/ -cp $output_filename1 ../images/README/ -cp $output_filename2 ../images/README/ -cp $output_filename_tiny_throughput ../images/README/ -cp $output_filename_small_throughput ../images/README/ -cp $output_filename_medium_throughput ../images/README/ -cp $output_filename_large_throughput ../images/README/ -cp $output_filename_tiny_qps ../images/README/ -cp $output_filename_small_qps ../images/README/ -cp $output_filename_medium_qps ../images/README/ -cp $output_filename_large_qps ../images/README/ diff --git a/benchmark/visualization/traefik-http-latency-plot.sh b/benchmark/visualization/traefik-http-latency-plot.sh deleted file mode 100755 index 112636f..0000000 --- a/benchmark/visualization/traefik-http-latency-plot.sh +++ /dev/null @@ -1 +0,0 @@ -./latency-plot.sh -m 40000 -o traefik-http-latency.png http-result-4c-traefik-tiny.txt http-result-4c-traefik-small.txt http-result-4c-traefik-medium.txt http-result-4c-traefik-large.txt diff --git a/benchmark/visualization/traefik-https-latency-plot.sh b/benchmark/visualization/traefik-https-latency-plot.sh deleted file mode 100755 index 9df7f0a..0000000 --- a/benchmark/visualization/traefik-https-latency-plot.sh +++ /dev/null @@ -1 +0,0 @@ -./latency-plot.sh -m 40000 -o traefik-https-latency.png https-result-4c-traefik-tiny.txt https-result-4c-traefik-small.txt https-result-4c-traefik-medium.txt https-result-4c-traefik-large.txt