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

Autor:	Pinto-Ríos, Juan, Calderón, Felipe, Leiva, Ariel, Hermosilla, Gabriel, Beghelli, Alejandra, Bórquez-Paredes, Danilo, Lozada, Astrid, Jara, Nicolás, Olivares, Ricardo, Saavedra, Gabriel
Předmět:	DEEP learning RESOURCE allocation OPTICAL fiber networks REINFORCEMENT learning HEURISTIC
Zdroj:	Complexity; 3/2/2023, p1-13, 13p
Abstrakt:	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. [ABSTRACT FROM AUTHOR]
Databáze:	Complementary Index
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