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MADDPG-Based Offloading Strategy for Timing-Dependent Tasks in Edge Computing

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  • Yuchen Wang

    (School of Information and Electrical Engineering, Hebei University of Engineering, Handan 056038, China
    Hebei Key Laboratory of Security and Protection Information Sensing and Processing, Handan 056038, China)

  • Zishan Huang

    (School of Information and Electrical Engineering, Hebei University of Engineering, Handan 056038, China
    Hebei Key Laboratory of Security and Protection Information Sensing and Processing, Handan 056038, China)

  • Zhongcheng Wei

    (School of Information and Electrical Engineering, Hebei University of Engineering, Handan 056038, China
    Hebei Key Laboratory of Security and Protection Information Sensing and Processing, Handan 056038, China)

  • Jijun Zhao

    (School of Information and Electrical Engineering, Hebei University of Engineering, Handan 056038, China
    Hebei Key Laboratory of Security and Protection Information Sensing and Processing, Handan 056038, China)

Abstract

With the increasing popularity of the Internet of Things (IoT), the proliferation of computation-intensive and timing-dependent applications has brought serious load pressure on terrestrial networks. In order to solve the problem of computing resource conflict and long response delay caused by concurrent application service applications from multiple users, this paper proposes an improved edge computing timing-dependent, task-offloading scheme based on Multi-Agent Deep Deterministic Policy Gradient (MADDPG) that aims to shorten the offloading delay and improve the resource utilization rate by means of resource prediction and collaboration among multiple agents to shorten the offloading delay and improve the resource utilization. First, to coordinate the global computing resource, the gated recurrent unit is utilized, which predicts the next computing resource requirements of the timing-dependent tasks according to historical information. Second, the predicted information, the historical offloading decisions and the current state are used as inputs, and the training process of the reinforcement learning algorithm is improved to propose a task-offloading algorithm based on MADDPG. The simulation results show that the algorithm reduces the response latency by 6.7% and improves the resource utilization by 30.6% compared with the suboptimal benchmark algorithm, and it reduces nearly 500 training rounds during the learning process, which effectively improves the timeliness of the offloading strategy.

Suggested Citation

  • Yuchen Wang & Zishan Huang & Zhongcheng Wei & Jijun Zhao, 2024. "MADDPG-Based Offloading Strategy for Timing-Dependent Tasks in Edge Computing," Future Internet, MDPI, vol. 16(6), pages 1-20, May.
  • Handle: RePEc:gam:jftint:v:16:y:2024:i:6:p:181-:d:1398494
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