Specific flow routing selection algorithm based on Self-Attention deep reinforcement learning
To effectively mitigate the negative impact of rerouting on the network in traditional traffic engineering mechanisms,this paper proposes a specific flow routing selection algorithm based on Self-Attention deep reinforcement learning,leveraging the global network perspective and management capabilities of software-defined networking,to reroute a small amount of traffic and achieve near-optimal performance.A neural network model with multi-scale fusion attention mechanism is used to extract features of traffic,and a centralized training distributed execution architecture is adopted to make real-time decisions based on the observed network state.The theoretical research and experimental results show that compared with traditional deep reinforcement learning algorithms and heuristic algorithms,the proposed algorithm has significant improvements in average load and end-to-end delay performance.
software-defined networkingmulti-agent deep reinforcement learningtraffic engineeringload balancing
国家科技期刊平台
NSTL
万方数据
维普
