A congestion control algorithm based on deep reinforcement learning in SDN data center networks

Pengfei Guo; Lixing Liang; Hongxiao Liu; Li Du

doi:10.1117/12.2639258

15 July 2022 A congestion control algorithm based on deep reinforcement learning in SDN data center networks

Pengfei Guo, Lixing Liang, Hongxiao Liu, Li Du

Proceedings Volume 12258, International Conference on Neural Networks, Information, and Communication Engineering (NNICE 2022); 122580V (2022) https://doi.org/10.1117/12.2639258
Event: International Conference on Neural Networks, Information, and Communication Engineering (NNICE 2022), 2022, Qingdao, China

Abstract

Aiming at the TCP Incast problem in Software Defined Networks (SDN) data center networks, a new congestion control algorithm based on deep reinforcement learning is proposed in this paper. By the way of continuously interacting between the agent and the environment to obtain the optimal strategy, the SDN controller plays the role as agent and many-to-one model of data center networks plays the role as environment. Under the condition that queue length of the buffer of the bottleneck switch does not exceed the preset congestion threshold, data transmission rates of multiple servers are trained using deep reinforcement learning algorithms, and finally the optimal transmission rates which will not cause congestion are acquired. The simulation results show that the algorithm proposed in this paper can effectively avoid congestion in many-to-one scenarios in SDN data center networks and can improve the overall performance of networks.

Citation Download Citation

Pengfei Guo, Lixing Liang, Hongxiao Liu, and Li Du "A congestion control algorithm based on deep reinforcement learning in SDN data center networks", Proc. SPIE 12258, International Conference on Neural Networks, Information, and Communication Engineering (NNICE 2022), 122580V (15 July 2022); https://doi.org/10.1117/12.2639258

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