An Approach to Combine the Power of Deep Reinforcement Learning with a Graph Neural Network for Routing Optimization

Electronics

Yuwei Wang¹  Bo Chen²  Di Zhu²  Peng Zhang² 

[1] Institute of Acoustics, Chinese Academy of Sciences, Beijing 100089, China;Institute of Information Technology, PLA Information Engineering University, Zhengzhou 450001, China;

关键词: deep reinforcement learning;    graph neural networks;    software-defined networking;    routing optimization;   

DOI  :  10.3390/electronics11030368

来源: DOAJ

【 摘 要 】

Routing optimization has long been a problem in the networking field. With the rapid development of user applications, network traffic is continuously increasing in dynamicity, making optimization of the routing problem NP-hard. Traditional routing algorithms cannot ensure both accuracy and efficiency. Deep reinforcement learning (DRL) has recently shown great potential in solving networking problems. However, existing DRL-based routing solutions cannot process the graph-like information in the network topology and do not generalize well when the topology changes. In this paper, we propose AutoGNN, which combines a GNN and DRL for the automatic generation of routing policies. In AutoGNN, the traffic distribution in the network topology is processed by a GNN, while a DRL framework is used to train the parameters of neural networks without human expertise. Our experimental results show that AutoGNN can improve the average end-to-end delay of the network by up to 19.7% as well as present more robustness against topology changes.

【 授权许可】

Unknown