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

Nour El Din El Sheikh, Esteban Paz, Juan Pinto, A. Beghelli
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引用次数: 12

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.
动态弹性光网络中的多频带配置:启发式和深度强化学习方法的比较研究
评估了一种启发式和深度强化学习方法在动态多波段弹性光网络资源分配中的阻塞性能。启发式是基于先前的一个建议,该建议优先使用波段C,然后按顺序使用波段L、S和E。深度强化学习方法使用深度q网络(DQN)智能体在不同的多频段场景下训练。结果表明,正如预期的那样,当从仅C波段场景移动到多波段场景(C+L, C+L+S, C+L+S+E)时,阻塞概率显著降低。然而,DQN代理并没有超越启发式。在以前的一些光网络研究中也观察到代理的较低性能,这表明需要进一步研究如何更好地配置代理并改进光网络环境中使用的网络表示。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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