ISSN 1000-1239 CN 11-1777/TP

Journal of Computer Research and Development ›› 2020, Vol. 57 ›› Issue (4): 688-698.doi: 10.7544/issn1000-1239.2020.20190837

Special Issue: 2020数据驱动网络专题

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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).

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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