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

TL;DR

This paper introduces a multiagent deep reinforcement learning-based cross-domain routing method for SDN, enabling real-time global network state awareness and optimal routing decisions, significantly improving network performance.

Contribution

It proposes a novel multiagent reinforcement learning approach combined with a message synchronization mechanism for real-time cross-domain routing in SDN.

Findings

Improves network throughput compared to traditional methods.

Reduces network delay and packet loss.

Enables real-time global network state acquisition.

Abstract

Message transmission and message synchronization for multicontroller interdomain routing in software-defined networking (SDN) have long adaptation times and slow convergence speeds, coupled with the shortcomings of traditional interdomain routing methods, such as cumbersome configuration and inflexible acquisition of network state information. These drawbacks make it difficult to obtain a global state information of the network, and the optimal routing decision cannot be made in real time, affecting network performance. This paper proposes a cross-domain intelligent SDN routing method based on a proposed multiagent deep reinforcement learning. First, the network is divided into multiple subdomains managed by multiple local controllers, and the state information of each subdomain is flexibly obtained by the designed SDN multithreaded network measurement mechanism. Then, a cooperative communication module is designed to realize message transmission and message synchronization between root and local controllers, and socket technology is used to ensure the reliability and stability of message transmission between multiple controllers to realize the real-time acquisition of global network state information. Finally, after the optimal intradomain and interdomain routing paths are adaptively generated by the agents in the root and local controllers, a prediction mechanism for the network traffic state is designed to improve the awareness of the cross-domain intelligent routing method and enable the generation of the optimal routing paths in the global network in real time. Experimental results show that the proposed cross-domain intelligent routing method can significantly improve the network throughput, reduce the network delay and the packet loss rate compared to the Dijkstra and OSPF routing methods.