Skip to content

Latency Equalization Policy of End-to-End Network Slicing Based on Reinforcement Learning

Notifications You must be signed in to change notification settings

moooontoo/Latency-Equalization-E2E-NS-RL

Folders and files

NameName
Last commit message
Last commit date

Latest commit

 

History

50 Commits
 
 
 
 
 
 
 
 
 
 

Repository files navigation

Latency-Equalization-E2E-NS-RL

Latency Equalization Policy of End-to-End Network Slicing Based on Reinforcement Learning | IEEE Journals & Magazine | IEEE Xplore

This repository contains Python scripts implementing the slicing resource allocation algorithms (RAN and CN) and latency equalization policies proposed in the paper "Latency Equalization Policy of End-to-End Network Slicing Based on Reinforcement Learning".

Run "DSDP.py"/"DTDP.py"/"static.py" to excute the latency equalization policies to generate the results.

The slicing algorithms in RAN and CN have been abstracted into functions (rlRAN, test) in "ran.py" and "Test.py".

"config.py" contains some parameter information for the wireless network.

The "user_plus" folder contains the updated rlRAN() , which only adjust the number of users.

The "data" folder contains "virtualnetworkTP.txt", which records the information of the SFCs. You can add new SFCs by following the same format.

About

Latency Equalization Policy of End-to-End Network Slicing Based on Reinforcement Learning

Resources

Stars

Watchers

Forks

Releases

No releases published

Packages

No packages published

Languages