Reconfigurable Network Topology Based on Deep Reinforcement Learning in Software-Defined Data-Center Networks

被引：0

作者：

Yang, Wen ^{[1
]}

Guo, Bingli ^{[1
]}

Shang, Yu ^{[2
]}

Huang, Shanguo ^{[1
]}

机构：

[1] Beijing Univ Posts & Telecommun, Sch Elect Engn, State Key Lab Informat Photon & Opt Commun, Beijing, Peoples R China

[2] Cyberspace Secur Key Lab Sichuan Prov, Chengdu 610041, Peoples R China

来源：

2020 ASIA COMMUNICATIONS AND PHOTONICS CONFERENCE (ACP) AND INTERNATIONAL CONFERENCE ON INFORMATION PHOTONICS AND OPTICAL COMMUNICATIONS (IPOC) | 2020年

关键词：

D O I：

暂无

中图分类号：

O43 [光学];

学科分类号：

070207 ; 0803 ;

摘要：

In this paper, a Deep-Reinforcement Learning (DRL) agent is implemented and evaluated to enable dynamic topology reconfiguration according to traffic fluctuations and proposes to minimize the network delay. (C) 2020 The Author(s)

引用

页数：3

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