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)
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页数：3
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