Routing (electronic design automation)

Gene

Engineering

Hierarchical routing

Network topology

Static routing

Robustness (evolution)

Biochemistry

Computer science

Distributed computing

Computer network

Routing protocol

Reinforcement learning

Routing domain

Artificial intelligence

Chemistry

Routing table

Topology (electrical circuits)

Artificial neural network

Electrical engineering

Electronics

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.

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