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Message transmission and message synchronization for multicontrollerinterdomain routing in software-defined networking (SDN) have long adaptationtimes and slow convergence speeds, coupled with the shortcomings of traditionalinterdomain routing methods, such as cumbersome configuration and inflexibleacquisition of network state information. These drawbacks make it difficult toobtain a global state information of the network, and the optimal routingdecision cannot be made in real time, affecting network performance. This paperproposes a cross-domain intelligent SDN routing method based on a proposedmultiagent deep reinforcement learning. First, the network is divided intomultiple subdomains managed by multiple local controllers, and the stateinformation of each subdomain is flexibly obtained by the designed SDNmultithreaded network measurement mechanism. Then, a cooperative communicationmodule is designed to realize message transmission and message synchronizationbetween root and local controllers, and socket technology is used to ensure thereliability and stability of message transmission between multiple controllersto realize the real-time acquisition of global network state information.Finally, after the optimal intradomain and interdomain routing paths areadaptively generated by the agents in the root and local controllers, aprediction mechanism for the network traffic state is designed to improve theawareness of the cross-domain intelligent routing method and enable thegeneration of the optimal routing paths in the global network in real time.Experimental results show that the proposed cross-domain intelligent routingmethod can significantly improve the network throughput, reduce the networkdelay and the packet loss rate compared to the Dijkstra and OSPF routingmethods.

A New Intelligent Cross-Domain Routing Method in SDN Based on a Proposed Multiagent Reinforcement Learning Algorithm