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A Multi-Agent RL Algorithm for Dynamic Task Offloading in D2D-MEC Network with Energy Harvesting.

Xin Mi1, Huaiwen He1, Hong Shen2

  • 1School of Computer, Zhongshan Institute, University of Electronic Science and Technology of China, Zhognshan 528400, China.

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Summary
This summary is machine-generated.

This study introduces a novel scheme for device-to-device assisted mobile edge computing (D2D-MEC) to minimize task delay. The multi-agent deep reinforcement learning algorithm (MAOC) dynamically manages task offloading and CPU frequency for energy harvesting devices.

Keywords:
D2D communicationMECdynamic task offloadingenergy harvestingmulti-agent reinforcement learning

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Area of Science:

  • Mobile Edge Computing
  • Wireless Communications
  • Artificial Intelligence

Background:

  • Delay-sensitive task offloading in D2D-MEC systems faces challenges from dynamic edge loads and variable energy harvesting.
  • Multi-slot tasks with varying processing and transmission rates add complexity to D2D-MEC systems.

Purpose of the Study:

  • To propose a joint dynamic task offloading and CPU frequency control scheme for delay-sensitive tasks in D2D-MEC systems.
  • To optimize average task delay by addressing the complexities of multi-slot tasks and energy harvesting.

Main Methods:

  • Modeling task arrival and service processes using queuing systems.
  • Utilizing device-to-device (D2D) communication to reduce edge server load and prevent network congestion.
  • Formulating average task delay optimization as a nonlinear integer programming problem.
  • Developing a multi-agent deep reinforcement learning (DRL) algorithm (MAOC) based on MAPPO for dynamic decision-making.

Main Results:

  • The MAOC algorithm, operating under a centralized training with decentralized execution (CTDE) framework, effectively minimizes average task delay.
  • Experimental results show swift convergence and high operational efficiency of the proposed MAOC algorithm.
  • MAOC outperforms existing baseline algorithms in delay-sensitive task offloading scenarios.

Conclusions:

  • The proposed joint dynamic task offloading and CPU frequency control scheme significantly improves performance in D2D-MEC systems.
  • The MAOC algorithm offers an efficient and effective solution for managing complex task offloading and resource allocation in edge computing environments.
  • This research contributes to the advancement of intelligent resource management in mobile edge computing for enhanced user experience.