- Title
- DQR: deep q-routing in software defined networks
- Creator
- Jalil, Syed Qaisar; Rehmani, Mubashir Husain; Chalup, Stephan
- Relation
- 2020 International Joint Conference on Neural Networks (IJCNN). Proceedings of 2020 International Joint Conference on Neural Networks (IJCNN) (Glasgow, UK 19-24 July, 2020)
- Publisher Link
- http://dx.doi.org/10.1109/IJCNN48605.2020.9206767
- Publisher
- Institute of Electrical and Electronics Engineers (IEEE)
- Resource Type
- conference paper
- Date
- 2020
- Description
- In this paper, we investigate the task of quality of service (QoS) routing in software defined networks (SDN). We consider delay, bandwidth, loss, and cost as QoS parameters. We propose a new deep reinforcement learning solution for greedy online QoS routing in SDN and call it Deep Q-Routing (DQR). DQR utilises a dueling deep Q-network with prioritised experience replay to compute a path for any source-destination pair request in the presence of multiple QoS metrics. In contrast to existing DRL-based routing methods, the proposed DQR method regards the task of routing as a discrete control problem and uses a reward function comprising weighted QoS parameters. Our simulation results show that DQR substantially improves end-to-end throughput compared to other existing learning based methods.
- Subject
- quality-of-serve routing; deep-q learning; software defined network
- Identifier
- http://hdl.handle.net/1959.13/1452684
- Identifier
- uon:44485
- Identifier
- ISBN:9781728169279
- Identifier
- ISSN:2161-4393
- Language
- eng
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