基于深度增强学习的软件定义网络路由优化机制  

A SDN Routing Optimization Mechanism Based on Deep Reinforcement Learning

作　　者：兰巨龙[1] 于倡和 胡宇翔[1] 李子勇 LAN Julong;YU Changhe;HU Yuxiang;LI Ziyong(National Digital Switching System Engineering&Technological Research Center,Zhengzhou 450002,China)

机构地区：[1]国家数字交换系统工程技术研究中心

出　　处：《电子与信息学报》2019年第11期2669-2674,共6页Journal of Electronics & Information Technology

基　　金：国家自然科学基金群体创新项目(61521003);国家自然科学基金(61502530)~~

摘　　要：为优化软件定义网络(SDN)的路由选路,该文将深度增强学习原理引入到软件定义网络的选路过程,提出一种基于深度增强学习的路由优化选路机制,用以削减网络运行时延、提高吞吐量等网络性能,实现连续时间上的黑盒优化,减少网络运维成本。此外,该文通过实验对所提出的路由优化机制进行评估,实验结果表明,路由优化机制具有良好的收敛性与有效性,较传统路由协议可提供更优的路由方案与实现更稳定的性能。In order to achieve routing optimization in the Software Defined Network(SDN)environment,deep reinforcement learning is imposed to the SDN routing process and a mechanism based on deep reinforcement learning is proposed to optimize routing.This mechanism can improve network performance such as delay,throughput,and realize black-box optimization in continuous time,which surely reduces network operation and maintenance costs.Besides,the proposed routing optimization mechanism is evaluated through a series of experiments.The experimental results show that the proposed SDN routing optimization mechanism has good convergence and effectiveness,and can provide better routing configurations and performance stability than traditional routing protocols.

关 键 词：软件定义网络 路由优化 深度增强学习 

分 类 号：TP393[自动化与计算机技术—计算机应用技术]