Evaluation of Deep Reinforcement Learning for Restoration in Optical Networks

C. Hernández-Chulde; R. Casellas; R. Martínez; R. Vilalta; R. Muñoz

doi:10.1364/OFC.2022.Th2A.19

Optical Fiber Communication Conference (OFC) 2022
Technical Digest Series (Optica Publishing Group, 2022),
paper Th2A.19
•https://doi.org/10.1364/OFC.2022.Th2A.19

Evaluation of Deep Reinforcement Learning for Restoration in Optical Networks

C. Hernández-Chulde, R. Casellas, R. Martínez, R. Vilalta, and R. Muñoz

Optical Fiber Communication Conference 2022

San Diego, California United States
6–10 March 2022
ISBN: 978-1-55752-466-9

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Poster Session II (Th2A)

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C. Hernández-Chulde, R. Casellas, R. Martínez, R. Vilalta, and R. Muñoz, "Evaluation of Deep Reinforcement Learning for Restoration in Optical Networks," in Optical Fiber Communication Conference (OFC) 2022, S. Matsuo, D. Plant, J. Shan Wey, C. Fludger, R. Ryf, and D. Simeonidou, eds., Technical Digest Series (Optica Publishing Group, 2022), paper Th2A.19.

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Abstract

A deep reinforcement learning-based agent is presented to perform autonomous lightpath restoration upon a link failure event. The agent is evaluated against other heuristic algorithms under different traffic load and failure duration scenarios.

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