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Load balancing strategy for SDN multi-controller clusters based on load prediction

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A Correction to this article was published on 26 October 2023

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Abstract

Software-defined networking (SDN) separates the control layer from the data layer, and decisions to manage the network are issued through a controller. The distributed SDN architecture is an effective solution addressing modern SDN architectures and allows multiple controllers to manage different parts of the network to ensure efficient and stable operation. To solve the problems of high switch migration cost, load imbalance, and inefficient load balancing in SDN multi-controller environments, we propose a deep learning-based controller load prediction switch migration strategy. This strategy uses a migration switch selection algorithm, a target controller selection algorithm, and a switch migration decision algorithm. Then, we propose a load balancing algorithm based on this decision algorithm. The final experimental results show that the load prediction switch migration strategy reduces the migration cost by 16% and 8%, respectively, compared with time-sharing switch migration and distributed decision migration strategies, reduces load variance from 0.02 to 0.004 compared with the distributed decision migration strategy, and improves load balancing efficiency by 27.6% compared with the time-sharing switch migration strategy.

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Acknowledgements

We thank LetPub (www.letpub.com) for its linguistic assistance during the preparation of this manuscript.

Funding

This work is partially supported by the Industry-university Research Innovation Foundation of Ministry of Education of China under Grant 2021FNA02007 and 2021FNA01001, partially supported by the Natural Science Foundation of Shandong Province under Grant ZR2020MF005, ZR2020MF006 and ZR2022LZH015.

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Authors and Affiliations

  1. Qingdao Institute of Software, College of Computer Science and Technology, China University of Petroleum (East China), Qingdao, 266580, China

    Junbi Xiao, Xingjian Pan, Jianhang Liu & Peiying Zhang

  2. College of Science, China University of Petroleum (East China), Qingdao, 266580, China

    Jian Wang

  3. Computer Science Department, Al al-Bayt University, Mafraq, 25113, Jordan

    Laith Abualigah

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  1. Junbi Xiao
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Correspondence to Junbi Xiao.

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Xiao, J., Pan, X., Liu, J. et al. Load balancing strategy for SDN multi-controller clusters based on load prediction. J Supercomput 80, 5136–5162 (2024). https://doi.org/10.1007/s11227-023-05658-6

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