Intelligent Bandwidth Allocation for Latency Management in NG-EPON using Reinforcement Learning Methods

Qi Zhou; Jingjie Zhu; Junwen Zhang; Zhensheng Jia; Bernardo Huberman; Gee-Kung Chang

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arXiv · 2001.07698

Intelligent Bandwidth Allocation for Latency Management in NG-EPON using Reinforcement Learning Methods

Qi Zhou·Jingjie Zhu·Junwen Zhang·Zhensheng Jia·Bernardo Huberman·Gee-Kung Chang

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Abstract

A novel intelligent bandwidth allocation scheme in NG-EPON using reinforcement learning is proposed and demonstrated for latency management. We verify the capability of the proposed scheme under both fixed and dynamic traffic loads scenarios to achieve <1ms average latency. The RL agent demonstrates an efficient intelligent mechanism to manage the latency, which provides a promising IBA solution for the next-generation access network.

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Qi Zhou, Jingjie Zhu, Junwen Zhang, Zhensheng Jia, Bernardo Huberman, Gee-Kung Chang. 2020-01-21. Intelligent Bandwidth Allocation for Latency Management in NG-EPON using Reinforcement Learning Methods. https://arxiv.org/abs/2001.07698

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