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Conference papers
Self-learning Routing for Optical Networks
Abstract : It is generally very difficult to optimize the routing policies in optical networks with dynamic traffic. Most widely-used routing policies, e.g., shortest path routing and least congested path (LCP) routing, are heuristic policies. Although the LCP is often regarded as the best-performing adaptive routing policy, we are often eager to know whether there exist better routing policies that surpass these heuristics in performance. In this paper, we propose a framework of reinforcement learning (RL) based routing scheme, that learns routing decisions during the interactions with the environment. With a proposed self-learning method, the RL agent can improve its routing policy continuously. Simulations on a ring-topology metro optical network demonstrate that, the proposed scheme outperforms the LCP routing policy.
https://hal.inria.fr/hal-03200673
Contributor : Hal Ifip Connect in order to contact the contributor
Submitted on : Friday, April 16, 2021 - 5:07:26 PM
Last modification on : Friday, April 16, 2021 - 5:38:24 PM
Long-term archiving on: : Saturday, July 17, 2021 - 7:11:47 PM
Contributor : Hal Ifip Connect in order to contact the contributor
Submitted on : Friday, April 16, 2021 - 5:07:26 PM
Last modification on : Friday, April 16, 2021 - 5:38:24 PM
Long-term archiving on: : Saturday, July 17, 2021 - 7:11:47 PM
Licence
Distributed under a Creative Commons Attribution 4.0 International License