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

Author

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

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.

Suggested Citation

  • Juan Pinto-Ríos & Felipe Calderón & Ariel Leiva & Gabriel Hermosilla & Alejandra Beghelli & Danilo Bórquez-Paredes & Astrid Lozada & Nicolás Jara & Ricardo Olivares & Gabriel Saavedra & Ning Cai, 2023. "Resource Allocation in Multicore Elastic Optical Networks: A Deep Reinforcement Learning Approach," Complexity, Hindawi, vol. 2023, pages 1-13, March.
    • Handle: RePEc:hin:complx:4140594
    DOI: 10.1155/2023/4140594
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