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Cost-efficient edge caching and Q-learning-based service selection policies in MEC

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Abstract

The paper focused on cache optimization and service selection algorithms in the cloud-edge environment. In order to solve the problem of cached content in edge servers, factors such as energy consumption and cost are considered, and finally a cache optimization model based on popularity was proposed. As for service selection, a Q-learning-based service selection algorithm is proposed to address the problems of dynamic task allocation and the optimization of Qos in the edge computing environment. The experimental results show that the proposed cache optimization and service selection algorithms in cloud-edge environment can better improve the cache hit ratio, minimize the transmission overhead, and ensure the server load balancing in the cloud-edge environment.

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Acknowledgements

The work was supported by Open Fund of Fujian Key Laboratory of Data Science and Statistics (Minnan Normal University) (No. 2020L0708), Open Fund of Fujian Key Laboratory of Energy Measurement (Fujian Metrology Institute) (NYJL-KFKT-2021-04), 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. Fujian Key Laboratory of Energy Measurement, Fujian Metrology Institute, Fuzhou, People’s Republic of China

    Menghui Wu & Chunlin Li

  2. Fujian Key Laboratory of Data Science and Statistics, Minnan Normal University, Zhangzhou, 363000, Fujian, People’s Republic of China

    Jingjing Guo & Chunlin Li

  3. School of Computer Science an Artificial Intelligence, Wuhan University of Technology, Wuhan, 430063, People’s Republic of China

    Chunlin Li & Youlong Luo

  4. College of Power Engineering, Naval University of Engineering, Wuhan, 430033, People’s Republic of China

    Jingjing Guo

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  1. Menghui Wu
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  2. Jingjing Guo
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Correspondence to Chunlin Li.

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Wu, M., Guo, J., Li, C. et al. Cost-efficient edge caching and Q-learning-based service selection policies in MEC. Wireless Netw 29, 285–301 (2023). https://doi.org/10.1007/s11276-022-03102-w

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