A Multi-Agents Survivable Deep Reinforced Routing and Spectrum Assignment Algorithm in Elastic Optical Network

Zhang, Zhidong; Luo, Zhihuan; Zhang, Wenchao; Yin, Shan; Huang, Shanguo; An, Ning; Luo, Zhen

doi:10.1109/iceiec54567.2022.9835064

Cited by 4 publications

(1 citation statement)

References 6 publications

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“…Amin et al [8] investigate the application of ML techniques in route optimization based on unsupervised learning, supervised learning, and reinforcement learning in software-defined networks. Zhang et al [9] propose a multi-intelligent survivable RSA algorithm based on deep reinforcement learning, utilizing rerouting to improve network survivability, reduce packet loss rate, and enhance resource utilization. Previous research has explored latency issues in fiber-optic networks.…”

Section: Introductionmentioning

confidence: 99%

Delay aware routing and spectrum allocation based on Q-learning

Zhao,

Hong,

Bai

2024

Advanced Fiber Laser Conference (AFL2023)

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This study proposes Q-learning-based dynamic routing algorithms to address routing and spectrum allocation challenges in elastic optical networks (EONs). An adaptive reinforcement learning framework is employed to enable real-time learning and decision-making under varying network conditions. The proposed Q-learning algorithm takes into consideration both the available spectrum and delay constraints in real-time to make informed decisions during routing and spectrum allocation, resulting in improved network capacity and reduced blocking rates. Following the Q-learning routing algorithm, two commonly used spectrum allocation methods, namely first fit and last fit, are applied. Simulation results demonstrate that the proposed method yields a lower blocking probability compared to using a combination of K shortest path routing and classical spectrum allocation strategies.

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Section: Introductionmentioning

confidence: 99%