Study on Topology Reconfiguration of Optical Interconnection Network in Data Center

YANG Wen; GUO Bing-li; YANG Hong-zhen; WANG Yan-bo; FAN Chao; MENG Ling-yu; HUANG Shan-guo

doi:10.13756/j.gtxyj.2022.01.003

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Study on Topology Reconfiguration of Optical Interconnection Network in Data Center

更新时间：2022-06-15

- Study on Topology Reconfiguration of Optical Interconnection Network in Data Center
- Study on Optical Communications Issue 1, Pages: 12-17,57(2022)
- 作者机构：
  
  1.北京邮电大学　电子工程学院　信息光子学与光通信国家重点实验室，北京　100867
  2.国网浙江省电力有限公司信息通信分公司，杭州　310036
- 作者简介：
- 基金信息：
- DOI：10.13756/j.gtxyj.2022.01.003
  CLC： TN915
- Published：10 February 2022，
  
  Received：18 January 2021，
扫描看全文
YANG Wen, GUO Bing-li, YANG Hong-zhen, et al. Study on Topology Reconfiguration of Optical Interconnection Network in Data Center. [J]. Study on Optical Communications (1):12-17,57(2022)
DOI：

YANG Wen, GUO Bing-li, YANG Hong-zhen, et al. Study on Topology Reconfiguration of Optical Interconnection Network in Data Center. [J]. Study on Optical Communications (1):12-17,57(2022) DOI： 10.13756/j.gtxyj.2022.01.003.

摘要

针对传统数据中心电互连网络在应对业务动态流量时存在适应性差的问题，文章提出并验证了一种可以根据网络流量波动进行网络拓扑自优化重构的机制。文章所提机制通过网络仿真系统与深度强化学习模型的迭代交互，实现了对拓扑结构与业务流量分布关系的持续训练，进而在实际系统中，深度强化学习模型，根据软件定义网络控制器实时收集的业务流量分布信息，实现了网络拓扑的自动优化重构，进而提升了网络性能。实验结果表明，针对给定的流量强度，采用深度强化学习进行训练后的模型可以一步输出优化的网络拓扑结构，降低了平均网络延迟和丢包率。

Abstract

Aiming at preventing the network performance deterioration caused by fixed link topology connections in optically switched networks

this paper proposes and validates an interconnection and control mechanism that can automatically optimize and reconfigure network topology according to the network traffic volume. This mechanism combines deep reinforcement learning with software defined network technology. Based on the real-time network performance parameters collected by the software-defined network control plane and the performance feedback of the network simulation system

the continuous training of the topology structure can be realized in the model of deep reinforcement learning. The automatic network topology reconfiguration is achieved with improved network performance. The experimental results show that for a given traffic intensity

the model trained by deep reinforcement learning can learn the topology reconstruction strategy and realize automatic selection. The optimal network topology structure can be achieved in one step

with reduced average network delay and average packet loss.

关键词

光互联网络深度强化学习网络拓扑重构

Keywords

optical interconnect networkdeep reinforcement learningnetwork topology reconfiguration

references

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