ISSN 1000-1239 CN 11-1777/TP

计算机研究与发展 ›› 2020, Vol. 57 ›› Issue (4): 688-698.doi: 10.7544/issn1000-1239.2020.20190837

所属专题: 2020数据驱动网络专题

• 网络技术 • 上一篇    下一篇

机器学习在SDN路由优化中的应用研究综述

王桂芝1,吕光宏1,贾吾财1,贾创辉1,张建申2   

  1. 1(四川大学计算机学院 成都 610065);2(7584部队 广西桂林 541001) (lghong@scu.edu.cn)
  • 出版日期: 2020-04-01
  • 基金资助: 
    国家自然科学基金项目(61373091)

A Review on the Application of Machine Learning in SDN Routing Optimization

Wang Guizhi1, Lü Guanghong1, Jia Wucai1, Jia Chuanghui1, Zhang Jianshen2   

  1. 1(College of Computer Science, Sichuan University, Chengdu 610065);2(Unit 7584, Guilin, Guangxi 541001)
  • Online: 2020-04-01
  • Supported by: 
    This work was supported by the National Natural Science Foundation of China (61373091).

摘要: 随着网络技术的迅速发展和新型应用的不断出现,网络数据的急剧增加导致网络管理变得极其复杂.传统网络中的设备多种多样,配置复杂,难于管理,而软件定义网络(software defined networking, SDN)这种新型网络架构的出现给网络管理带来了曙光,该架构摆脱了硬件设备对网络的限制,使网络具有灵活、可编程性等优点.一个好的路由机制影响着整个网络的性能,软件定义网络的集中控制特性给机器学习在路由机制方面的应用带来了新的研究方向.首先论述了SDN路由优化的现状,然后从监督学习和强化学习2个方面概述了近年来机器学习在SDN路由方面的研究,最后为了满足不同应用的服务质量(quality of service, QoS)以及不同用户的体验质量(quality of experience, QoE),提出了数据驱动认知路由的发展趋势.通过赋予网络节点感知、记忆、查找、决策、推理、解释等认知行为,加快寻路过程,优化路由选择,完善网络管理.

关键词: 软件定义网络, 监督学习, 强化学习, 路由优化, 服务质量, 体验质量, 数据驱动, 认知

Abstract: With the rapid development of network technology and the continuous emergence of new applications, the sharp increase in network data makes network management extremely complicated. Devices in traditional networks are diverse, complex in configuration, and difficult to manage, but the appearance of a new network architecture, such as software defined networking (SDN), brings dawn to network management, which gets rid of the limitation of hardware equipment to the network, and makes the network have the advantages of flexibility, programmability and so on. A good routing mechanism affects the performance of the whole network, the centralized control characteristics of SDN bring new research directions to the application of machine learning in routing mechanisms. First this paper discusses the current status of SDN routing optimization, and then summarizes the research on machine learning in SDN routing in recent years from the aspects of supervised learning and reinforcement learning. Finally, in order to meet the QoS (quality of service) of different applications and QoE (quality of experience) of different users, this paper puts forward the development trend of the data driven cognitive route. Giving the network nodes with cognitive behaviors such as perception, memory, search, decision-making, reasoning, explanation and so on, can speed up the path-finding process, optimize the route selection and improve the network management.

Key words: software defined networking (SDN), supervised learning, reinforcement learning, routing optimization, quality of service (QoS), quality of experience (QoE), data driven, cognitive

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