Complexity (Jan 2023)

Resource Allocation in Multicore Elastic Optical Networks: A Deep Reinforcement Learning Approach

  • Juan Pinto-Ríos,
  • Felipe Calderón,
  • Ariel Leiva,
  • Gabriel Hermosilla,
  • Alejandra Beghelli,
  • Danilo Bórquez-Paredes,
  • Astrid Lozada,
  • Nicolás Jara,
  • Ricardo Olivares,
  • Gabriel Saavedra

DOI
https://doi.org/10.1155/2023/4140594
Journal volume & issue
Vol. 2023

Abstract

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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.