Improving End-To-End Latency Fairness Using a Reinforcement-Learning-Based Network Scheduler

Improving End-To-End Latency Fairness Using a Reinforcement-Learning-Based Network Scheduler

In services such as metaverse, which should provide a constant quality of service (QoS) regardless of the user’s physical location, the end-to-end (E2E) latency must be fairly distributed over any flow in the network. To this end, we propose a reinforcement learning (RL)-based scheduler for minimizi...

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Bibliographic Details
Main Authors: Juhyeok Kwon, Jihye Ryu, Jee Hang Lee, Jinoo Joung
Format: Article
Language:English
Published: MDPI AG 2023-03-01
Series:Applied Sciences
Subjects:
min-max criterion
reinforcement learning
double-deep Q-learning
prioritized experience replay
end-to-end latency
fairness
Online Access:https://www.mdpi.com/2076-3417/13/6/3397

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https://www.mdpi.com/2076-3417/13/6/3397

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