Multi-band Environments for Optical Reinforcement Learning Gym for Resource Allocation in Elastic Optical Networks

Patricia Morales, Patricia Franco, Astrid Lozada, Nicolas Jara, Felipe Calderon, Juan Pinto-Rios, Ariel Leiva

Resultado de la investigación: Capítulo del libro/informe/acta de congresoContribución a la conferenciarevisión exhaustiva

Resumen

The use of additional fiber bands for optical communications-known as Multi-band or Band-division multiplexing (BDM)-allows to increase the traffic served in transparent optical networks. In recent years, many proposals have emerged as a solution for resource allocation in such multi-band architectures. This work presents a novel approach based on reinforcement learning (RL) techniques to accommodate multi-band elastic optical network resources. Two new environments were implemented and added to the Optical-RL-Gym toolkit considering four scenarios with different band availability. Six agents were tested in four real network topologies, contrasting their episode rewards on a large number of training steps. Results show Trust Region Policy Optimization (TRPO) as the best performing agent, with consistent output across all the scenarios and network topologies considered. In addition, we illustrate the blocking probability behavior in relation to the traffic load, and band usage distribution, allowing further discussions.

Idioma originalInglés
Título de la publicación alojada25th International Conference on Optical Network Design and Modelling, ONDM 2021
EditorialInstitute of Electrical and Electronics Engineers Inc.
ISBN (versión digital)9783903176331
DOI
EstadoPublicada - 28 jun. 2021
Publicado de forma externa
Evento25th International Conference on Optical Network Design and Modelling, ONDM 2021 - Gothenburg, Suecia
Duración: 28 jun. 20211 jul. 2021

Serie de la publicación

Nombre25th International Conference on Optical Network Design and Modelling, ONDM 2021

Conferencia

Conferencia25th International Conference on Optical Network Design and Modelling, ONDM 2021
País/TerritorioSuecia
CiudadGothenburg
Período28/06/211/07/21

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