Assessment of a Latency-aware Routing and Spectrum Assignment Mechanism Based on Deep Reinforcement Learning

descriptionPublicationkeyboard_double_arrow_right Article , Conference object 13 Sep 2021Publisher:IEEEJournal:2021 European Conference on Optical Communication (ECOC)

Authors: Carlos Hernández-Chulde; Ramon Casellas; Ricardo Martínez 0001; Ricard Vilalta; Raul Muñoz 0001;

doi: 10.1109/ecoc52684.2021.9605919 , 10.5281/zenodo.6482554 , 10.5281/zenodo.6482555

Assessment of a Latency-aware Routing and Spectrum Assignment Mechanism Based on Deep Reinforcement Learning

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Abstract

We present a solution based on deep reinforcement learning (DRL) that jointly addresses spectrum allocation and latency constraint in EONs. The results show that using a simple network representation, this strategy outperforms typical K-Shortest Path heuristic approach and previous DRLbased approaches.

The research leading to these results has received funding from MINECO Project AURORAS (RTI2018-099178) and Spanish Thematic Network Go2Edge (RED2018-102585-T).

Related Organizations

Huawei Technologies (China)
China (People's Republic of)
Centre Tecnologic De Telecomunicacions De Catalunya
Spain

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