A Graph Reinforcement Learning Routing Scheme Based on Nodes and Edges Co-Embedding

In recent years, machine learning (ML) has been widely used to address software-defined networking routing problems. However, existing ML-based methods still face challenges such as the lack of representation for network links, inadequate consideration of quality-of-service metrics in routing policies and difficulties in achieving globally optimal under distributed routing control. To overcome these limitations, this paper proposes a Graph Reinforcement Learning Routing scheme (GRL-R) that simultaneously embeds nodes and links features to enable network local awareness. And a comprehensive reward function is designed with taking account of packet loss, delay and throughput, then globally optimal routing policies are offered by the combination of a centrally deployed agent and a safe learning mechanism. Finally, experiments conducted on Abilene, GEANT2 and OS3E show that the proposed method outperforms the baselines.