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Multi-Agent Deep Reinforcement Learning Based Dynamic Task Offloading in a Device-to-Device Mobile-Edge Computing

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Summary
This summary is machine-generated.

This study introduces a new multi-agent deep reinforcement learning algorithm for dynamic task offloading in device-to-device mobile edge computing systems. It significantly reduces task delay and dropped tasks for delay-sensitive applications.

Keywords:
D2Ddelay constraintdynamic matchingmobile edge computingmulti-agent reinforcement learning

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Area of Science:

  • Computer Science
  • Electrical Engineering
  • Artificial Intelligence

Background:

  • Device-to-device (D2D) communication enables direct task offloading between mobile devices (MDs), optimizing resource utilization.
  • Mobile edge computing (MEC) systems leverage D2D for efficient processing of delay-sensitive tasks.
  • Existing research insufficiently addresses dynamic partitioning and task offloading in D2D-MEC with stochastic arrivals and multi-time-slot execution.

Purpose of the Study:

  • To propose a novel algorithm for dynamic task offloading in D2D-MEC systems.
  • To minimize the long-term average delay of delay-sensitive tasks under deadline constraints.
  • To introduce a dynamic partitioning scheme for active and idle devices in D2D-MEC.

Main Methods:

  • Formulated task offloading as a queue-based optimization problem.
  • Modeled the problem as a Markov decision process (MDP).
  • Applied multi-agent deep reinforcement learning (DRL) using multi-agent proximal policy optimization (MAPPO) with a centralized training with decentralized execution (CTDE) framework.

Main Results:

  • The proposed multi-agent DRL algorithm demonstrates efficiency and fast convergence.
  • Achieved an 11.0% reduction in average task completion delay compared to single-agent DRL.
  • Reduced the ratio of dropped tasks by 17.0%.

Conclusions:

  • The novel algorithm effectively minimizes task delay and dropped tasks in D2D-MEC systems.
  • The dynamic partitioning scheme enhances performance by considering stochastic task arrivals and multi-time-slot execution.
  • The approach is highly relevant for sensor networks and delay-sensitive applications requiring high Quality of Experience (QoE) and adherence to Service-Level Agreements (SLAs).