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MAARS: Multiagent Actor-Critic Approach for Resource Allocation and Network Slicing in Multiaccess Edge Computing.

Ducsun Lim1, Inwhee Joe1

  • 1Department of Computer Software, Hanyang University, Seoul 04763, Republic of Korea.

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Summary
This summary is machine-generated.

This study introduces a novel multiagent actor-critic resource scheduling (MAARS) algorithm for optimizing resource allocation and network slicing in multiaccess edge computing (MEC) networks, significantly improving task completion rates.

Keywords:
multiaccess edge computingnetwork slicingreinforcement learningresource allocation

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Area of Science:

  • Computer Science
  • Telecommunications Engineering
  • Artificial Intelligence

Background:

  • Multiaccess Edge Computing (MEC) networks face challenges in resource allocation and network slicing to meet diverse service demands.
  • Efficiently managing resources across core and edge networks is crucial for maximizing task completion rates.

Purpose of the Study:

  • To develop a novel algorithm for optimizing resource allocation and network slicing in MEC networks.
  • To enhance the task completion rate of user-computing tasks in dynamic MEC environments.

Main Methods:

  • A multiagent actor-critic resource scheduling (MAARS) algorithm is proposed, integrating multiagent deep deterministic policy gradient (MADDPG) and soft actor-critic (SAC).
  • The algorithm decomposes the problem into efficient core-to-edge slicing (ECS) and autonomous resource slicing (ARS) for collaborative and dynamic resource management.

Main Results:

  • MAARS demonstrated superior performance compared to benchmark algorithms (heuristic-based, DQN, A2C) in simulations.
  • Key performance indicators improved, including task completion rates, resource utilization, and convergence speed.

Conclusions:

  • The MAARS algorithm provides a scalable and efficient framework for resource optimization and network slicing in MEC.
  • This research offers practical benefits for real-world MEC deployments and establishes a new performance benchmark.