Journal of South China University of Technology(Natural Science Edition) ›› 2022, Vol. 50 ›› Issue (4): 65-72.doi: 10.12141/j.issn.1000-565X.210373

Special Issue: 2022年电子、通信与自动控制

• Electronics, Communication & Automation Technology • Previous Articles     Next Articles

Research on Survivability Technology for Software Defined Network Based on Q-Learning Algorithm

WANG Weifa1 ZHANG Daming2 DAI Yi3 KE Feng4 FENG Suili5   

  1. 1． School of Electronic and Information Engineering，South China University of Technology，Guangzhou 510640，Guangdong，China;
    2． The 7th Ｒesearch Institute of China Electronics Technology Group Corporation，Guangzhou 510310，Guangdong，China
  • Received:2021-06-07 Revised:2021-09-15 Online:2022-04-25 Published:2021-10-08
  • Contact: 王炜发 (1981-)，男，在职博士生，高级工程师，主要从事专网通信、软件定义网络等研究 E-mail:13676263085@163. com
  • About author:王炜发 (1981-)，男，在职博士生，高级工程师，主要从事专网通信、软件定义网络等研究
  • Supported by:
    Guangdong Natural Science Foundation;Guangzhou City Science and Technology Plan Project?

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Abstract: Aiming at the link invulnerability problem of software-defined networks, in order to make data transmission more robust, a destructive strategy based on Q learning algorithm is designed. This strategy chooses to use the interruption probability of each link in the network as a measure The indicator, through the Q learning algorithm, finds a path with low interruption probability as the backup path according to the network situation, so that when the network transmission fails, the backup path can be automatically switched to improve the anti-destructive performance. The Q learning algorithm is compared with the existing algorithm, and the advantages and disadvantages of each are analyzed. The experimental simulation results show that compared with the ant colony algorithm, the average throughput of the Q learning algorithm can be increased by 15%, and the average network transmission interruption probability can be reduced by 38%; compared with the shortest path algorithm (with backup), the average throughput should be improved 16.5%, the interruption probability of network transmission is reduced by 43% on average. It can be seen that the survivability technology based on Q-learning proposed in this article can greatly improve the survivability of SDN networks.

Key words: Software Defined Network, Q-learning Algorithm, Survivability

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