Topology design for data center networks using deep reinforcement learning

Qi, H., Shu, Z. and Chen, X. ORCID: https://orcid.org/0000-0001-9267-355X (2023) Topology design for data center networks using deep reinforcement learning. In: 2023 International Conference on Information Networking (ICOIN), 11-14 January 2023, Bangkok, Thailand, pp. 251-256. doi: 10.1109/ICOIN56518.2023.10048955

Abstract/Summary

This paper is concerned with the topology design of data center networks (DCNs) for low latency and fewer links using deep reinforcement learning (DRL). Starting from a Kvertex-connected graph, we propose an interactive framework with single-objective and multi-objective DRL agents to learn DCN topologies for given node traffic matrices by choosing link matrices to represent the states and actions as well as using the average shortest path length together with action penalty terms as reward feedback. Comparisons with commonly used DCN topologies are given to show the effectiveness and merits of our method. The results reveal that our learned topologies could achieve lower delay compared with common DCN topologies. Moreover, we believe that the method can be extended to other topology metrics, e.g., throughput, by simply modifying the reward functions.

Item Type	Conference or Workshop Item (Paper)
URI	https://reading-clone.eprints-hosting.org/id/eprint/116491
Identification Number/DOI	10.1109/ICOIN56518.2023.10048955
Refereed	Yes
Divisions	Science > School of Mathematical, Physical and Computational Sciences > Department of Computer Science
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