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arXiv 2303.07572cs.NI
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基于多智能体强化学习算法的SDN智能跨域路由方法

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

Miao Ye, Linqiang Huang, Xiaofang Deng, Yong Wang, Qiuxiang Jiang, Hongbing Qiu, Peng Wen

摘要:

针对软件定义网络(SDN)中多控制器域间路由的消息传输和消息同步存在自适应时间长、收敛速度慢的问题,以及传统域间路由方法配置繁琐、网络状态信息获取不灵活等缺点。这些缺点使得网络的全局状态信息难以获得,不能实时的做出最优路由决策,影响网络性能。提出了一种基于多智能体深度强化学习的SDN跨域智能路由方法。首先,将网络划分为多个子域,由多个本地控制器管理,通过设计的SDN多线程网络测量机制灵活获取每个子域的状态信息。然后,设计了协同通信模块,实现根控制器和本地控制器之间的消息传输和消息同步,并利用socket技术保证多个控制器之间消息传输的可靠性和稳定性,实现全局网络状态信息的实时获取。最后,在根控制器和本地控制器中的代理自适应生成最优域内和域间路由路径后,设计了网络流量状态的预测机制,提高了跨域智能路由方法的感知能力,使全局网络中最优路由路径的生成实时的化。实验结果表明,与Dijkstra和OSPF路由方法相比,本文提出的跨域智能路由方法能够显著提高网络吞吐量,降低网络延迟和丢包率。

英文摘要:

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.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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