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Maximizing the number of completed tasks in MEC considering time and energy constraints

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Abstract

Many tasks generated by mobile user devices are computation-intensive and latency-sensitive, such as autonomous driving and video analysis. However, due to limited energy and computing capacity, a user device may not be able to complete its task within a given time, leading to a poor user experience. Mobile edge computing (MEC) can address this challenge by offloading tasks to edge servers with stronger computing capacity and more resources for execution, which can save energy of user devices and reduce the task computation time. Different offloading strategies will impact the number of tasks completed, latency, energy overhead and so on. This paper investigates the problem of maximizing the number of completed tasks while minimizing the average completion time, energy overhead and cost in MEC under both time and energy constraints. To solve the problem, we develop the mayfly genetic algorithm (MGA), which jointly optimizes task offloading locations and ratios, central processor unit (CPU) frequencies of user devices and computing capacities allocated to user devices by edge servers. Simulation experiments indicate that MGA outperforms state-of-the-art algorithms in terms of the number of completed tasks.

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Availability of data and material

The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.

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Acknowledgements

This work has been partially supported by the Key Project of Scientific Research Plan of Hubei Provincial Department of Education (Grant No. D20201102).

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The authors have not disclosed any funding.

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Author notes

  1. Jing Liu, Chunhua Deng, Cen Chen, Keqin Li have contributed equally to this work.

Authors and Affiliations

  1. Department Computer Science and Technology, Wuhan University of Science and Technology, Wuhan, 430000, China

    Haijian Yu, Jing Liu & Chunhua Deng

  2. Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System, Wuhan, 430000, China

    Haijian Yu, Jing Liu & Chunhua Deng

  3. Institute for Infocomm Research, Singapore, 999002, Singapore

    Cen Chen

  4. State University of New York, New Paltz, New York, 12561, USA

    Keqin Li

Authors
  1. Haijian Yu
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  2. Jing Liu
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  3. Chunhua Deng
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  4. Cen Chen
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  5. Keqin Li
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All authors contributed to the study conception and design.

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Correspondence to Jing Liu or Chunhua Deng.

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Yu, H., Liu, J., Deng, C. et al. Maximizing the number of completed tasks in MEC considering time and energy constraints. Soft Comput 27, 15095–15110 (2023). https://doi.org/10.1007/s00500-023-08695-8

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  • DOI: https://doi.org/10.1007/s00500-023-08695-8

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