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Multi-band provisioning in dynamic elastic optical networks: a comparative study of a heuristic and a deep reinforcement learning approach

Sheikh, Nour El Din El; Paz, Esteban; Pinto, Juan; Beghelli, Alejandra; (2021) Multi-band provisioning in dynamic elastic optical networks: a comparative study of a heuristic and a deep reinforcement learning approach. In: Proceedings of the 2021 International Conference on Optical Network Design and Modeling (ONDM). IEEE: Gothenburg, Sweden. Green open access

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Abstract

The blocking performance of a heuristic and a deep reinforcement learning approach for resource provisioning in a dynamic multi-band elastic optical network is evaluated. The heuristic is based on a previous proposal that prioritises the use of band C, then L, S, and E, in that order. The deep reinforcement learning approach uses a deep Q-network (DQN) agent trained on different multi-band scenarios. Results show, as expected, a significant decrease in blocking probability when moving from the C-band only scenario to the multi-band scenarios (C+L, C+L+S, C+L+S+E). However, the DQN agent did not outperform the heuristic. The lower performance of the agent, also observed in some previous works in optical networks, highlights the need for further research on how to better configure agents and improve the network representation used by the optical network environments.

Type: Proceedings paper
Title: Multi-band provisioning in dynamic elastic optical networks: a comparative study of a heuristic and a deep reinforcement learning approach
Event: 2021 International Conference on Optical Network Design and Modeling (ONDM)
Dates: 28 Jun 2021 - 1 Jul 2021
ISBN-13: 978-3-9031-7633-1
Open access status: An open access version is available from UCL Discovery
DOI: 10.23919/ondm51796.2021.9492334
Publisher version: https://doi.org/10.23919/ondm51796.2021.9492334
Language: English
Additional information: This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions.
Keywords: provisioning, reinforcement learning, multi-band optical networks, elastic optical networks
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.ucl.ac.uk/id/eprint/10161007
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