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Deep reinforcement learning-based resource allocation and seamless handover in multi-access edge computing based on SDN

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Abstract

With the access devices that are densely deployed in multi-access edge computing environments, users frequently switch access devices when moving, which causes the imbalance of network load and the decline of service quality. To solve the problems above, a seamless handover scheme for wireless access points based on perception is proposed. First, a seamless handover model based on load perception is proposed to solve the unbalanced network load, in which a seamless handover algorithm for wireless access points is used to calculate the access point with the highest weight, and a software-defined network controller controls the switching process. A joint allocation method of communication and computing resources based on deep reinforcement learning is proposed to minimize the terminal energy consumption and the system delay. A resource allocation model is based on minimizing terminal energy consumption, and system delay is built. The optimal value of task offloading decision and resource allocation vector are calculated with deep reinforcement learning. Experimental results show that the proposed method can reduce the network load and the task execution cost.

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Acknowledgements

The work was supported by the National Natural Science Foundation (NSF) under Grants (No. 61873341, 61771354), Key Research and Development Plan of Hubei Province (No. 2020BAB102), and Open Foundation of Industrial Software Engineering Technology Research and Development Center of Jiangsu Education Department (No. ZK19-04-04). Any opinions, findings, and conclusions are those of the authors and do not necessarily reflect the views of the above agencies.

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Authors and Affiliations

  1. Department of Computer Science, Wuhan University of Technology, Wuhan, 430063, People’s Republic of China

    Chunlin Li, Yong Zhang & Youlong Luo

  2. Industrial Software Engineering Technology Research and Development Center of Jiangsu Education Department, Nanjing Institute of Industry Technology, Nanjing, People’s Republic of China

    Chunlin Li

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  1. Chunlin Li
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Correspondence to Chunlin Li.

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Li, C., Zhang, Y. & Luo, Y. Deep reinforcement learning-based resource allocation and seamless handover in multi-access edge computing based on SDN. Knowl Inf Syst 63, 2479–2511 (2021). https://doi.org/10.1007/s10115-021-01590-4

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