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A jointly non-cooperative game-based offloading and dynamic service migration approach in mobile edge computing

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Abstract

With the increase in the use of compute-intensive applications, the demand to continuously boost the efficiency of data processing increases. Offloading the compute-intensive application tasks to the edge servers can effectively solve problems for resource-constrained mobile devices. However, the computation offloading may increase network load and transmission delay, which will influence the user experience. On the other hand, the unceasing distance change between the local device and edge server could also affect the service quality due to user mobility. This paper proposes the offloading and service migration methods for compute-intensive applications to deal with these issues. First, the fine-grained computation offloading algorithm based on a non-cooperative game is proposed. The overhead on both the local side and edge side is analyzed. Moreover, the service migration path selection based on the Markov decision process is proposed by considering user mobility, energy cost, migration cost, available storage, and bandwidth. The optimal service migration path is selected according to the Markov decision process, which can improve service quality. Experiment results show that our proposed offloading strategy performs better in reducing energy consumption by more than 10% and latency by more than 6.2%, compared with other baseline algorithms, and saving mobile device energy and reducing task response time, saving over 10% of time and energy consumption compared to similar algorithms. The proposed service migration scheme can reduce migration times and maintain a success rate of more than 90% while guaranteeing service continuity in a multi-user scenario.

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Acknowledgements

The work was supported by National Natural Science Foundation of China (NSFC) under grants (No.62171330), open fund of Guangxi Key Laboratory of Machine Vision and Intelligent Control (No.2022A02), the open Foundation of Intelligent Manufacturing Fujian University Application Technology Engineering Center (No.ZNZZ22-01), Guangxi Key Laboratory of Hybrid Computation and IC Design Analysis (No.GXIC20-02), Open Fund of Key Laboratory of Flight Techniques and Flight Safety, CAAC (No. FZ2022KF18), Open Fund of Key Laboratory of AI and Information Processing (Hechi University), Education Department of Guangxi Zhuang Autonomous Region (No.2022GXZDSY012), Open Fund of Key Laboratory of Electromechanical Equipment Security in Western Complex Environment for State Market Regulation (No.CQTJ-XBJD-KFKT202201). The Higher Educational Key Laboratory for Flexible Manufacturing Equipment Integration of Fujian Province (Xiamen Institute of Technology) (No. pklfmeik20220002).

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Authors and Affiliations

  1. Intelligent Manufacturing Fujian University Application Technology Engineering Center, Quanzhou Vocational and Technical University, Quanzhou, People’s Republic of China

    Qingzhe Zhang

  2. Guangxi Key Laboratory of Machine Vision and Intelligent Control, Wuzhou University, Wuzhou, 543002, People’s Republic of China

    Chunlin Li

  3. Guangxi Key Laboratory of Hybrid Computation and IC Design Analysis, Guangxi University for Nationalities, Nanning, 530006, People’s Republic of China

    Chunlin Li

  4. Key Laboratory of Flight Techniques and Flight Safety, CAAC, Civil Aviation Flight University of China, Deyang, People’s Republic of China

    Qingzhe Zhang

  5. Key Laboratory of Electromechanical Equipment Security in Western Complex Environment for State Market Regulation, Chongqing Special Equipment Inspection and Research Institute, Chongqing, People’s Republic of China

    Youlong Luo

  6. The Higher Educational Key Laboratory for Flexible Manufacturing Equipment Integration of Fujian Province (Xiamen Institute of Technology), Xiamen, 361021, China

    Qingzhe Zhang

  7. Key Laboratory of AI and Information Processing (Hechi University), Education Department of Guangxi Zhuang Autonomous Region, Hechi, People’s Republic of China

    Chunlin Li

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

    Chunlin Li, Qingzhe Zhang & Youlong Luo

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  1. Chunlin Li
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  3. Youlong Luo
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Contributions

C.L., Q.Z., and Y.L. were designed the study, developed the methodology, performed the analysis, and wrote the manuscript. C.L., Q.Z. were collected the data.

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Correspondence to Chunlin Li.

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Li, C., Zhang, Q. & Luo, Y. A jointly non-cooperative game-based offloading and dynamic service migration approach in mobile edge computing. Knowl Inf Syst 65, 2187–2223 (2023). https://doi.org/10.1007/s10115-022-01822-1

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  • DOI: https://doi.org/10.1007/s10115-022-01822-1

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