Computer Engineering and Applications ›› 2021, Vol. 57 ›› Issue (12): 93-98.DOI: 10.3778/j.issn.1002-8331.2003-0423

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SDN Routing Optimization Algorithm Based on Reinforcement Learning

CHE Xiangbei, KANG Wenqian, OUYANG Yuhong, YANG Kehan, LI Jian   

  1. 1.Shenzhen Power Supply Bureau Co., Ltd., Shenzhen, Guangdong 510800, China
    2.School of Computer Science, Beijing University of Posts and Telecommunications, Beijing 100876, China
  • Online:2021-06-15 Published:2021-06-10



  1. 1.深圳供电局有限公司,广东 深圳 510800
    2.北京邮电大学 计算机学院,北京 100876


Aiming at the network routing optimization in SDN controller, a routing optimization algorithm is designed based on the PPO model in reinforcement learning. The algorithm can adjust the reward function for different optimization goals to dynamically update the routing strategy, and this algorithm does not depend on any specific network state and has very good generalization performance. Because of adopting the strategy method in reinforcement learning, the control of routing strategy is more elaborate than various Q-learning-based algorithms. Based on Omnet++ simulation software, the performance of the algorithm is evaluated through experiments. Compared with the traditional shortest path routing algorithm, the average delay and end-to-end maximum delay of this routing optimization algorithm on the Sprint structure network are reduced by 29.3% and 17.4%, respectively and throughput rate is increased by 31.77%. The experimental result shows that this PPO-based SDN routing control algorithm not only has good convergence, but also has better performance and stability than the shortest path routing algorithm and the Q-learning based QAR routing algorithm.

Key words: software-defined network, reinforcement learning, SDN routing optimization



关键词: 软件定义网络, 强化学习, SDN路由优化