光通信技术2024，Vol.48Issue(3)：7-12,6.DOI:10.13921/j.cnki.issn1002-5561.2024.03.002
基于自注意力深度强化学习的特定流路由选择算法
Specific flow routing selection algorithm based on Self-Attention deep reinforcement learning
摘要
Abstract
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.关键词
软件定义网络/多智能体深度强化学习/流量工程/负载均衡Key words
software-defined networking/multi-agent deep reinforcement learning/traffic engineering/load balancing分类
信息技术与安全科学引用本文复制引用
袁帅,张慧,蔡安亮,沈建华..基于自注意力深度强化学习的特定流路由选择算法[J].光通信技术,2024,48(3):7-12,6.基金项目
国家自然科学青年基金项目(62301284)资助 （62301284）
南京邮电大学企业委托研发重点课题(KH0020322072)资助. （KH0020322072）
