In this demonstration, we will deploy Buoyant Enterprise for Linkerd and the Orders application across two k3d
clusters deployed in a flat network, and will demonstrate how to deploy the Buoyant Enterprise for Linkerd Multi-Cluster Extension and enable High Availability Zonal Load Balancing (HAZL). We'll then take a look at how HAZL works to keep network traffic in-zone where possible by exploring some different traffic, load and availability situations.
Buoyant Enterprise for Linkerd
Buoyant Enterprise for Linkerd is an enterprise-grade service mesh for Kubernetes. It makes Kubernetes applications reliable, secure, and cost-effective without requiring any changes to application code. Buoyant Enterprise for Linkerd contains all the features of open-source Linkerd, the world's fastest, lightest service mesh, plus additional enterprise-only features such as:
- High-Availability Zonal Load Balancing (HAZL)
- Security Policy Management
- FIPS-140-2/3 Compliance
- Lifecycle Automation
- Buoyant Cloud
- Mesh Expansion
Plus:
- Enterprise-Hardened Images
- Software Bills of Materials (SBOMs)
- Strict SLAs Around CVE Remediation
- 24x7x365 Support With SLAs
- Quarterly Outcomes and Strategy Reviews
We're going to try out Linkerd's Multi-Cluster Expansion and High-Availability Zonal Load Balancing (HAZL) in this Demonstration.
SAY SOMETHING HERE
High Availability Zonal Load Balancing (HAZL) is a dynamic request-level load balancer in Buoyant Enterprise for Linkerd that balances HTTP and gRPC traffic in environments with multiple availability zones. For Kubernetes clusters deployed across multiple zones, HAZL can dramatically reduce cloud spend by minimizing cross-zone traffic.
For more information on HAZL, click here.
In this demonstration, we will deploy Buoyant Enterprise for Linkerd and the Orders application across two k3d
clusters deployed in a flat network, and will demonstrate how to enable High Availability Zonal Load Balancing (HAZL). We'll then take a look at how HAZL works to keep network traffic in-zone where possible by exploring some different traffic, load and availability situations.
In this demonstration, we're going to do the following:
- Deploy two
k3d
Kubernetes clusters that share a common network - Deploy Buoyant Enterprise for Linkerd with HAZL disabled on the clusters
- Deploy the Orders application to the clusters, half on one cluster and half on the other, to generate multi-zonal traffic
- Deploy and configure the Linkerd Multicluster extension
- Monitor traffic from the Orders application, with HAZL disabled
- Enable High Availability Zonal Load Balancing (HAZL)
- Monitor traffic from the Orders application, with HAZL enabled
- Scale traffic in various zones
- Monitor traffic from the Orders application
- Observe effects of HAZL
- Observe interaction with Horizontal Pod Autoscaling
- Add latency to one zone
- Monitor traffic from the Orders application
- Observe effects of HAZL
- Observe interaction with Horizontal Pod Autoscaling
- Simulate an outage in one zone
- Monitor traffic from the Orders application
- Observe effects of HAZL
- Observe interaction with Horizontal Pod Autoscaling
- Restore outage and remove latency
- Monitor traffic from the Orders application
- Observe effects of HAZL
- Observe interaction with Horizontal Pod Autoscaling
- Restore Orders application to initial state
If you'd like to follow along, you're going to need the following:
- Docker
- Helm
- k3d
- step
- The
kubectl
command must be installed and working - The
watch
command must be installed and working, if you want to use it - The
kubectx
command must be installed and working, if you want to use it - Buoyant Enterprise for Linkerd License
- The Demo Assets, from GitHub
All prerequisites must be installed and working properly before proceeding. The instructions in the provided links will get you there. A trial license for Buoyant Enterprise for Linkerd can be obtained from the link above. Instructions on obtaining the demo assets from GitHub are below.
The top-level contents of the repository look like this:
.
├── README.md <-- This README
├── bel-manual-deploy.md <-- Instructions on how to manually deploy BEL
├── certs <-- Directory for the TLS root certificates
├── cluster <-- The k3d cluster configuration files live here
├── deploy-clusters.sh <-- Script to stand up the clusters, install Linkerd and Orders
├── deploy-multicluster.sh <-- Script to deploy the Buoyant Enterprise for Linkerd Multi-Cluster Extension
├── destroy-clusters.sh <-- Script to destroy the cluster environment
├── hazl.md <-- Detailed information on HAZL
├── images <-- Images for the README
├── mc.awk <-- Progfile for the awk command we use when setting up multi-cluster
├── orders -> orders-hpa <-- Soft link
├── orders-hpa <-- The Orders application, with Horizontal Pod Autoscaling
├── orders-nohpa <-- The Orders application, without Horizontal Pod Autoscaling
├── policy.yaml <-- Grants service-mirror access to the core Linkerd control plane
└── traffic_check.sh <-- Script to monitor application traffic
The repository contains the following automation:
deploy-clusters.sh
- Script to stand up the clusters, install Linkerd and Orders
deploy-multicluster.sh
- Script to deploy the multicluster extension to the clusters
destroy-clusters.sh
- Script to destroy the cluster environments and clean up contexts
If you choose to use the deploy-clusters.sh
script, make sure you've created the settings.sh
file and run source settings.sh
to set your environment variables. For more information, see the Obtain Buoyant Enterprise for Linkerd (BEL) Trial Credentials and Log In to Buoyant Cloud instructions.
This repository contains two k3d
cluster configuration files:
.
├── cluster
│  ├── orders.yaml
│  └── warehouse.yaml
These will be used to deploy our two clusters.
This repository includes the Orders application, which generates traffic across multiple availability zones in our Kubernetes cluster, allowing us to observe the effect that High Availability Zonal Load Balancing (HAZL) has on traffic.
We're going to deploy the orders-*
applications on the orders
cluster and the warehouse-*
applications on the warehouse
cluster.
.
├── orders -> orders-hpa
├── orders-hpa
│  ├── orders
│  │  ├── kustomization.yaml
│  │  ├── ns.yaml
│  │  ├── orders-central.yaml
│  │  ├── orders-east.yaml
│  │  └── orders-west.yaml
│  └── warehouse
│  ├── kustomization.yaml
│  ├── ns.yaml
│  ├── server.yaml
│  ├── warehouse-boston.yaml
│  ├── warehouse-chicago.yaml
│  └── warehouse-oakland.yaml
├── orders-nohpa
│  ├── orders
│  │  ├── kustomization.yaml
│  │  ├── ns.yaml
│  │  ├── orders-central.yaml
│  │  ├── orders-east.yaml
│  │  └── orders-west.yaml
│  └── warehouse
│  ├── kustomization.yaml
│  ├── ns.yaml
│  ├── server.yaml
│  ├── warehouse-boston.yaml
│  ├── warehouse-chicago.yaml
│  └── warehouse-oakland.yaml
The repository contains two copies of the Orders application:
orders-hpa
: HAZL version of the orders app with Horizontal Pod Autoscalingorders-nohpa
: HAZL version of the orders app without Horizontal Pod Autoscaling
An orders
soft link points to the hpa
version of the application (orders -> orders-hpa
), with Horizontal Pod Autoscaling. We will reference the orders
soft link in the steps. If you want to use the nohpa
version of the application, without Horizontal Pod Autoscaling, deploy the Orders application from the orders-nohpa
directory, or recreate the orders
soft link, pointing to the orders-nohpa
directory.
Demonstration 1: Deploy a Kubernetes Cluster With Buoyant Enterprise for Linkerd, With HAZL Disabled
First, we'll deploy a Kubernetes cluster using k3d
and deploy Buoyant Enterprise for Linkerd (BEL).
We're going to use the provided deploy-clusters.sh
script for this. If you'd like to do it by hand, the instructions are here.
From the k3d-multicluster-flat-network-hazl
directory, execute the deploy-clusters.sh
script:
./deploy-clusters.sh
This will create your k3d
clusters and shared network, will deploy Buoyant Enterprise for Linkerd on both clusters, and will deploy the Orders application across both clusters.
Let's see how we deploy the Buoyant Enterprise for Linkerd Multi-Cluster Extension!
Explain
kubectl get cm coredns -n kube-system -o yaml --context orders -o yaml | grep -Ev "creationTimestamp|resourceVersion|uid" > coredns.yaml
sed -i .orig 's/host.k3d.internal/host.k3d.internal\ kubernetes/g' coredns.yaml
more coredns.yaml
kubectl apply -f coredns.yaml -n kube-system --context orders
kubectl rollout restart deploy coredns -n kube-system --context orders
kubectl get cm coredns -n kube-system -o yaml --context orders -o yaml | grep kubernetes
Installing the Multi-Cluster Extension
source settings.sh
helm repo add linkerd-buoyant https://helm.buoyant.cloud
helm repo update
helm install linkerd-multicluster \
--create-namespace \
--namespace linkerd-multicluster \
--kube-context orders \
--set linkerd-multicluster.gateway.enabled=false \
--set license=$BUOYANT_LICENSE \
linkerd-buoyant/linkerd-enterprise-multicluster
linkerd --context=orders multicluster check
helm install linkerd-multicluster \
--create-namespace \
--namespace linkerd-multicluster \
--kube-context warehouse \
--set linkerd-multicluster.gateway.enabled=false \
--set license=$BUOYANT_LICENSE \
linkerd-buoyant/linkerd-enterprise-multicluster
linkerd --context=warehouse multicluster check
Configure the Linkerd Multi-Cluster Policy
"As of the Linkerd 2.11 release, the Linkerd multi-cluster extension also includes a policy configuration that prevents unauthorized access to pods running in the linkerd-multicluster namespace. This policy configuration only grants access to the core Linkerd control plane by default. If you’re using the Linkerd multi-cluster extension with Buoyant Cloud, you’ll need to add the following configuration to the linkerd-multicluster namespace."
If you'd like to see the Linkerd Multi-Cluster Policy:
more policy.yaml
Apply the Linkerd Multi-Cluster Policy to the orders
cluster from the included manifest:
kubectl apply -f policy.yaml --context orders
linkerd --context=warehouse multicluster link --cluster-name warehouse --gateway=false > multicluster-link-orig.yaml
KC1=`linkerd --context=warehouse multicluster link --cluster-name warehouse --gateway=false | grep kubeconfig: | uniq | awk {'print $2'}` ; KC2=`echo $KC1 | base64 -d | sed 's/0\.0\.0\.0/kubernetes/g' | base64` ; awk -f mc.awk "$KC1" "$KC2" multicluster-link-orig.yaml > multicluster-link.yaml
kubectl apply -f multicluster-link.yaml --context orders
kubectl get links -A --context=orders
kubectl get svc -A --context=orders
kubectl get svc -A --context=warehouse
kubectl --context=warehouse label svc -n orders fulfillment mirror.linkerd.io/exported=remote-discovery
kubectl get svc -A --context=orders
Now that BEL is fully deployed, we're going to need some traffic to observe.
In this scenario, we're delving into the operations of an online business specializing in Hacky Sack products. This business relies on a dedicated orders application to manage customer orders efficiently and to ensure that these orders are promptly dispatched to warehouses for shipment. To guarantee high availability and resilience, the system is distributed across three geographical availability zones: zone-east
, zone-central
, and zone-west
. This strategic distribution ensures that the system operates smoothly, maintaining a balanced and steady state across different regions.
For the deployment of the Orders application, the business utilizes a modern infrastructure approach by employing Kubernetes. To further enhance the system's reliability and observability, Buoyant's Enterprise Linkerd service mesh is deployed on our cluster. Remember, Linkerd provides critical features such as dynamic request routing, service discovery, and comprehensive monitoring, which are instrumental for maintaining the health and performance of the Orders application across the clusters. Deploying the Orders application to a fresh Kubernetes cluster, augmented with Buoyant Enterprise Linkerd, signifies a significant step towards achieving robust, scalable, and highly available online business operations, ensuring that customers receive their Hacky Sack products without delays.
We don't know it yet, but our business, and the magic of hacky sack, are about to be featured on an episode of a popular sitcom tonight and orders are going to spike!
With the Orders application deployed across our clusters, we have some traffic to work with.
Let's take a look at traffic flow without HAZL enabled in Buoyant Cloud. This will give us a more visual representation of our baseline traffic. Head over to Buoyant Cloud, and take a look at the contents of the orders
namespace in the Topology tab.
We can see that traffic from each orders
deployment is flowing to all three warehouse
deployments, and that about 2/3 of total traffic is out of zone. Latency is hovering around 80 ms per zone, and Requests By Warehouse has some variation over time. All deployments are currently scaled to one replica.
Let's see what happens when we enable HAZL.
Let's take a look at how quick and easy we can enable High Availability Zonal Load Balancing (HAZL).
Remember, to make adjustments to your BEL deployment simply edit and re-apply the previously-created linkerd-control-plane-config-hazl.yaml
manifest. We're going to enable the - -ext-endpoint-zone-weights
in the additionalArgs
for now, by uncommenting it in the manifest:
Edit the linkerd-control-plane-config-hazl.yaml
file:
vi linkerd-control-plane-config-hazl.yaml
Apply the ControlPlane CRD config to have the Linkerd BEL operator update the Linkerd control plane configuration, and enable HAZL on the hazl
cluster only:
kubectl apply -f linkerd-control-plane-config-hazl.yaml --context orders
kubectl apply -f linkerd-control-plane-config-hazl.yaml --context warehouse
Now, we can see the effect HAZL has on the traffic in our multi-az cluster.
Let's take a look at what traffic looks like with HAZL enabled, using Buoyant Cloud. This will give us a more visual representation of the effect of HAZL on our traffic.
With HAZL enabled, we see traffic stay in zone, at 50 requests per second. Cross-AZ traffic drops to zero, latency by zone drops to around 50 ms and Requests By Warehouse smooths out. Application load stays consistent and all deployments remain at one replica.
A popular sitcom aired an episode in which the characters, a bunch of middle-aged Generation X folks, flash back to their teenage years, and remember the joy they experienced playing hacky sack together. Our characters decide they're going to relive those wonderful hacky sack memories, and go online to order supplies, featuring our website and products. Jackpot!
Let's simulate what that looks like. The first thing we see is an uptick of orders in zone-east
, as they're the first to watch the episode.
We can increase traffic in zone-east
by scaling the orders-east
deployment. Let's scale to 10 replicas.
kubectl scale -n orders deploy orders-east --replicas=10 --context orders
Let's see the results of scaling orders-east
:
watch -n 1 kubectl get deploy,hpa -n orders --context warehouse
Use CTRL-C
to exit the watch command.
Let's take a look at what the increased traffic looks like in Buoyant Cloud. This will give us a more visual representation of the effect of HAZL on our traffic.
After scaling orders-east
to 10 replicas, traffic remains in the same AZ and latency holds steady at around 50 ms. Order success remains at 100%.
The middle of the country really liked our hacky sacks! Order volume is running more than double what we saw in zone-east
.
We can increase traffic in zone-central
by scaling the orders-central
deployment. Let's scale to 25 replicas.
kubectl scale -n orders deploy orders-central --replicas=25 --context orders
Let's see the results of scaling orders-central
:
watch -n 1 kubectl get deploy,hpa -n orders --context warehouse
Use CTRL-C
to exit the watch command.
Let's take a look at what the increased traffic looks like in Buoyant Cloud. This will give us a more visual representation of the effect of HAZL on our traffic.
Again, after scaling orders-central
to 25 replicas, all traffic remains in the same AZ and latency holds steady at around 50 ms. Order success remains at 100%. Both warehouse-boston
and warehouse-chicago
have autoscaled to 3 replicas.
By now, word of the episode is all over social media, and when the episode airs in Pacific time, orders in zone-west
spike.
We can increase traffic in zone-west
by scaling the orders-west
deployment. Let's scale to 30 replicas.
kubectl scale -n orders deploy orders-west --replicas=30 --context orders
Let's see the results of scaling orders-west
:
watch -n 1 kubectl get deploy,hpa -n orders --context warehouse
Use CTRL-C
to exit the watch command.
Let's take a look at what the increased traffic looks like in Buoyant Cloud. This will give us a more visual representation of the effect of HAZL on our traffic.
Once more, after scaling orders-west
to 30 replicas, all traffic remains in the same AZ and latency holds steady at around 50 ms. Order success remains at 100%. Both warehouse-boston
, warehouse-chicago
and warehouse-oakland
have autoscaled to 3 replicas.
So far, everything has gone right. What about when things go wrong?
Unfortunately, we've had some network issues creep in at our Chicago warehouse!
We can increase latency in zone-central
by editing the warehouse-config
configmap, which has a setting for latency.
kubectl edit -n orders cm/warehouse-config --context warehouse
You'll see:
data:
blue.yml: |
color: "#0000ff"
averageResponseTime: 0.020
green.yml: |
color: "#00ff00"
averageResponseTime: 0.020
red.yml: |
color: "#ff0000"
averageResponseTime: 0.020
The colors map to the warehouses as follows:
- Red: This is the Oakland warehouse (
warehouse-oakland
) - Blue: This is the Boston warehouse (
warehouse-boston
) - Green: This is the Chicago warehouse (
warehouse-chicago
)
Change the value of averageResponseTime
under green.yml
from 0.020
to 0.920
. Save and exit.
We need to restart the warehouse-chicago
deployment to pick up the changes:
kubectl rollout restart -n orders deploy warehouse-chicago --context warehouse
Let's take a look at what the increased latency looks like in Buoyant Cloud. This will give us a more visual representation of the effect of HAZL on our traffic in response to increased latency.
HAZL steps in and does what it needs to do, redirecting a portion of the traffic from orders-central
across AZs to warehouse-oakland
to keep success rate at 100%. Latency increases in orders-central
, but HAZL adjusts.
More bad news! The latency we've been experiencing is about to turn into an outage!
We can simulate this by scaling the warehouse-chicago
deployment. Let's scale to 0 replicas.
kubectl scale -n orders deploy warehouse-chicago --replicas=0 --context warehouse
Let's see the results of scaling warehouse-chicago
to 0:
watch -n 1 kubectl get deploy,hpa -n orders --context warehouse
Use CTRL-C
to exit the watch command.
Let's take a look at what the increased traffic looks like in Buoyant Cloud. This will give us a more visual representation of the effect of HAZL on our traffic.
Once again, HAZL steps in and does what it needs to do, redirecting all of the traffic from orders-central
across AZs to warehouse-oakland
to keep success rate at 100%. Latency drops in orders-central
as we are no longer sending traffic to warehouse-chicago
, which added latency.
Good news! The latency and outage is about to end!
We can simulate this by scaling the warehouse-chicago
deployment. Let's scale to 1 replica. The Horizontal Pod Autoscaler will take over from there.
kubectl scale -n orders deploy warehouse-chicago --replicas=1 --context warehouse
We also need to edit the warehouse-config
configmap, and set the latency to match the other warehouse
deployments.
kubectl edit -n orders cm/warehouse-config --context warehouse
We need to restart the warehouse-chicago
deployment to pick up the changes:
kubectl rollout restart -n orders deploy warehouse-chicago --context warehouse
Let's see the results of scaling warehouse-chicago
and restarting the deployment:
watch -n 1 kubectl get deploy,hpa -n orders --context warehouse
Use CTRL-C
to exit the watch command.
Let's take a look at what the service restoration looks like in Buoyant Cloud. This will give us a more visual representation of the effect of HAZL on our traffic.
We can see things have returned to 100% in-zone traffic, with latency back to about 50 ms across the board and success rates at 100%. HPA has 3 replicas of the warehouse
deployments per zone.
Now that we're finished, let's reset the Orders application back to its initial state.
kubectl apply -k orders/orders --context orders
kubectl apply -k orders/warehouse --context warehouse
Let's see the results of the reset:
watch -n 1 kubectl get deploy,hpa -n orders --context warehouse
Use CTRL-C
to exit the watch command.
If we give things a minute to settle back down, we should see all traffic back in zone and request rates back to 50.
Everything has returned to the initial state with HAZL enabled. All deployments are a single replica, all traffic remains in-zone, and success rates are 100%. Looking good!
You can clean up the Demonstration environment by running the included script:
./destroy-clusters.sh
Checking our work:
k3d cluster list
We shouldn't see our demo-cluster-orders-hazl
cluster.
In this hands-on Demonstration, we deployed Buoyant Enterprise for Linkerd and demonstrated how to enable High Availability Zonal Load Balancing (HAZL). We also took a look at how HAZL works to keep network traffic in-zone where possible by exploring some different traffic, load and availability situations.
Thank you for taking a journey with HAZL and Buoyant!