Abstract: The routing behavior of traditional SDN traffic engineering models based on deep reinforcement learning(DRL) is often unpredictable,and the traditional DRL-based routing scheme is unreliable if it simply applies the DRL algorithm to the communication network system.This paper proposes an online parallel SDN routing optimization algorithm based on DRL,so as to reliably utilize the trial-and-error DRL routing algorithm to improve network performance.The algorithm uses a combination of online parallel routing decision-making and offline training in the SDN framework to solve the SDN routing optimization problem.This method can alleviate the reliability issues arising from the deep reinforcement learning model’s lack of convergence and the exploration process.To a certain extent,it can also alleviate the negative impact of the unexplainability of the deep reinforcement lear-ning intelligent routing model and the unreliability of routing behavior under network emergencies.This paper evaluates the performance of the online parallel SDN routing optimization algorithm by extensive experiments on a real network topology.The experimental results show that the network performance of the proposed algorithm is better than the traditional DRL-based routing algorithm and OSPF algorithm.

Key words: Software-defined network, Deep reinforcement learning, Routing optimization