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Zeng, X., Wu, L., Li, Z. and Jing, Y. (2021) Deep Reinforcement Learning with Graph Convolutional Networks for Load Balancing in SDN-Based Data Center Networks. 2021 IEEE 18th International Computer Conference on Wavelet Active Media Technology and Information Processing (ICCWAMTIP), Chengdu, 17-19 December 2021, 344-352.

has been cited by the following article:

• TITLE: Review of Load Balancing Mechanisms in SDN-Based Data Centers

  AUTHORS: Qin Du, Xin Cui, Haoyao Tang, Xiangxiao Chen

  KEYWORDS: Software Defined Network, Data Center, Load Balancing, Traffic Conflicts, Traffic Scheduling

  JOURNAL NAME: Journal of Computer and Communications, Vol.12 No.1, January 15, 2024

  ABSTRACT: With the continuous expansion of the data center network scale, changing network requirements, and increasing pressure on network bandwidth, the traditional network architecture can no longer meet people’s needs. The development of software defined networks has brought new opportunities and challenges to future networks. The data and control separation characteristics of SDN improve the performance of the entire network. Researchers have integrated SDN architecture into data centers to improve network resource utilization and performance. This paper first introduces the basic concepts of SDN and data center networks. Then it discusses SDN-based load balancing mechanisms for data centers from different perspectives. Finally, it summarizes and looks forward to the study on SDN-based load balancing mechanisms and its development trend.