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An Approach to Combine the Power of Deep Reinforcement Learning with a Graph Neural Network for Routing Optimization
| Content Provider | MDPI |
|---|---|
| Author | Chen, Bo Zhu, Di Wang, Yuwei Zhang, Peng |
| Copyright Year | 2022 |
| Description | 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. |
| Starting Page | 368 |
| e-ISSN | 20799292 |
| DOI | 10.3390/electronics11030368 |
| Journal | Electronics |
| Issue Number | 3 |
| Volume Number | 11 |
| Language | English |
| Publisher | MDPI |
| Publisher Date | 2022-01-26 |
| Access Restriction | Open |
| Subject Keyword | Electronics Industrial Engineering Deep Reinforcement Learning Graph Neural Networks Software-defined Networking Routing Optimization |
| Content Type | Text |
| Resource Type | Article |
