电讯技术2025，Vol.65Issue(1)：18-24,7.DOI:10.20079/j.issn.1001-893x.231114002
基于图神经网络的SDN路由算法优化
Optimization of SDN Routing Algorithm Based on Graph Neural Network
摘要
Abstract
For the problems that existing routing schemes are not suitable for learning graph structure information and have poor adaptability to unfamiliar topologies,a software defined network(SDN)routing algorithm based on graph neural network called G-PPO is proposed.Proximal policy optimization(PPO)reinforcement learning algorithm is introduced to realize model training,massage passing neural network(MPNN)is used to learn network topology,and route adjustment is completed by adjusting link weights.G-PPO effectively combines the perception ability of graph neural network to network topology information with the autonomous learning ability of deep reinforcement learning to improve the performance of routing strategies.Experimental results show that compared with related algorithms,the proposed algorithm has the best average delay,packet loss rate,higher network link utilization rate and throughput.In three different topologies,the throughput and packet loss rate of proposed algorithm are improved by at least 10.5%and at most 95.6%respectively compared with those of other algorithms,indicating that the algorithm has better ability to adapt to different network topologies.关键词
软件定义网络/路由优化/图神经网络/深度强化学习/近端策略优化Key words
software defined network/routing optimization/graph neural network/deep reinforcement learning/proximal policy optimization分类
信息技术与安全科学引用本文复制引用
张晓莉,汤颖琪,宋婉莹..基于图神经网络的SDN路由算法优化[J].电讯技术,2025,65(1):18-24,7.基金项目
国家自然科学基金青年科学基金项目(61901358) （61901358）
