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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.
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https://hal.inria.fr/hal-03200673
Contributor : Hal Ifip <>
Submitted on : Friday, April 16, 2021 - 5:07:26 PM
Last modification on : Friday, April 16, 2021 - 5:38:24 PM

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Yue-Cai Huang, Jie Zhang, Siyuan Yu. Self-learning Routing for Optical Networks. 23th International IFIP Conference on Optical Network Design and Modeling (ONDM), May 2019, Athens, Greece. pp.467-478, ⟨10.1007/978-3-030-38085-4_40⟩. ⟨hal-03200673⟩

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