Pinto-Ríos, J;
Calderón, F;
Leiva, A;
Hermosilla, G;
Beghelli, A;
Bórquez-Paredes, D;
Lozada, A;
... Saavedra, G; + view all
(2023)
Resource Allocation in Multicore Elastic Optical Networks: A Deep Reinforcement Learning Approach.
Complexity
, 2023
, Article 4140594. 10.1155/2023/4140594.
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Abstract
A deep reinforcement learning (DRL) approach is applied, for the first time, to solve the routing, modulation, spectrum, and core allocation (RMSCA) problem in dynamic multicore fiber elastic optical networks (MCF-EONs). To do so, a new environment was designed and implemented to emulate the operation of MCF-EONs - taking into account the modulation format-dependent reach and intercore crosstalk (XT) - and four DRL agents were trained to solve the RMSCA problem. The blocking performance of the trained agents was compared through simulation to 3 baselines RMSCA heuristics. Results obtained for the NSFNet and COST239 network topologies under different traffic loads show that the best-performing agent achieves, on average, up to a four-times decrease in blocking probability with respect to the best-performing baseline heuristic method.
| Type: | Article |
|---|---|
| Title: | Resource Allocation in Multicore Elastic Optical Networks: A Deep Reinforcement Learning Approach |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1155/2023/4140594 |
| Publisher version: | https://doi.org/10.1155/2023/4140594 |
| Language: | English |
| Additional information: | Copyright © 2023 Juan Pinto-R´ıos et al. Tis is an open access article distributed under the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Electronic and Electrical Eng |
| URI: | https://discovery-pp.ucl.ac.uk/id/eprint/10167052 |
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