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MADDPG-based joint optimization of task partitioning and computation resource allocation in mobile edge computing

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Abstract

The continual development of mobile edge computing efficiently solves the problem that mobile devices are unable to handle computation-intensive tasks due to their computation capacity and battery restrictions. In this paper, we consider mobile awareness and dynamic battery charging in a multi-user and multi-server mobile edge computing system, where various tasks are generated successively on the user devices. Servers act as learning agents and collaborate with user devices to develop task partitioning and computation resource allocation strategies. With the purpose of decreasing task failure rate and improving system utility in the long term, which is closely related to latency, energy consumption, and server cost, optimal strategies are demanded by the system. We model the joint optimization problem as a multi-agent Markov decision process game. And a deep reinforcement learning method based on the multi-agent deep deterministic policy gradient algorithm is proposed, which employs neural networks and works in a centralized training and decentralized execution manner to optimize the strategies. Finally, simulation results demonstrate the effectiveness of our proposed algorithm in terms of reducing task failure rate and improving system utility.

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Data availability

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

Notes

  1. For ease of presentation, each base station is a MEC server in the following.

  2. User devices and mobile devices are used interchangeably throughout the paper.

  3. Abbreviation for Mobile Devices.

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Acknowledgements

This work is supported by the National Natural Science Foundation of China under Grant Nos.: 61877007 and 12061054 and Fundamental Research Funds for the Central Universities under No.DUT20GJ205.

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Authors and Affiliations

  1. School of Software Technology, Dalian University of Technology, Dalian, 116620, China

    Kun Lu, Rong-Da Li, Ming-Chu Li & Guo-Rui Xu

  2. Key Laboratory for Ubiquitous Network and Service Software of Liaoning Province, Dalian, 116620, China

    Kun Lu, Rong-Da Li, Ming-Chu Li & Guo-Rui Xu

Authors
  1. Kun Lu
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  2. Rong-Da Li
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  3. Ming-Chu Li
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  4. Guo-Rui Xu
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Contributions

KL contributed to conceptualization, methodology, and writing—original draft preparation, and provided software. RDL was involved in writing—original draft preparation, methodology, visualization, and validation and provided software. MCL contributed to resources, supervision, project administration, funding acquisition, and writing—review and editing. GRX provided software and was involved in writing—review and editing.

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Correspondence to Kun Lu.

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Lu, K., Li, RD., Li, MC. et al. MADDPG-based joint optimization of task partitioning and computation resource allocation in mobile edge computing. Neural Comput & Applic 35, 16559–16576 (2023). https://doi.org/10.1007/s00521-023-08527-8

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  • DOI: https://doi.org/10.1007/s00521-023-08527-8

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