关键词: 软件定义网络, Q学习算法, 抗毁

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