DROM: Optimizing the Routing in Software-Defined Networks With Deep Reinforcement Learning

被引：123

作者：

Yu, Changhe ^{[1
]}

Lan, Julong ^{[1
]}

Guo, Zehua ^{[2
]}

Hu, Yuxiang ^{[1
]}

机构：

[1] Natl Digital Switching Syst Engn & Technol R&D Ct, Zhengzhou 450002, Henan, Peoples R China

[2] Univ Minnesota Twin Cities, Minneapolis, MN 55455 USA

来源：

IEEE ACCESS | 2018年 / 6卷

基金：

中国国家自然科学基金;

关键词：

Deep reinforcement learning; routing optimization; software-defined networking;

D O I：

10.1109/ACCESS.2018.2877686

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

This paper proposes DROM, a deep reinforcement learning mechanism for Software-Defined Networks (SDN) to achieve a universal and customizable routing optimization. DROM simplifies the network operation and maintenance by improving the network performance, such as delay and throughput, with a black-box optimization in continuous time. We evaluate the DROM with experiments. The experimental results show that DROM has the good convergence and effectiveness and provides better routing configurations than existing solutions to improve the network performance, such as reducing the delay and improving the throughput.

引用

页码：64533 / 64539

页数：7

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