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Intelligent Task Dispatching and Scheduling Using a Deep Q-Network in a Cluster Edge Computing System.

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  • 1Department of Industrial ICT Engineering, Dong-Eui University, Busan 47340, Korea.

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Summary
This summary is machine-generated.

This study introduces an intelligent task dispatching model using deep reinforcement learning for AI-powered IoT applications in mobile edge computing. The model minimizes task response times and optimizes resource allocation, outperforming existing methods.

Keywords:
clusteringdeep reinforcement learningedge computingtask offloading

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Edge Computing

Background:

  • Intelligent IoT applications with AI require significant computing resources and low latency in dynamic Mobile Edge Computing (MEC) environments.
  • Existing task dispatching methods struggle to efficiently manage resources and meet the demands of AI tasks in MEC.

Purpose of the Study:

  • To propose an intelligent task dispatching model for AI-enabled IoT applications in MEC.
  • To minimize average task response time and optimize resource allocation within a cluster edge system.
  • To enhance the computational capabilities of edge nodes and the overall cluster edge system.

Main Methods:

  • Formulated resource allocation as a Markov Decision Process (MDP).
  • Employed deep reinforcement learning, specifically a Deep Q-Network (DQN) algorithm, to develop the intelligent task dispatching model.
  • Utilized Kubernetes technology for implementing the cluster edge system.

Main Results:

  • The proposed DQN-based model demonstrated superior convergence performance regarding average task completion time compared to Random, Least Load, and Round-Robin methods.
  • The model achieved a higher task completion rate utilizing the same cluster edge system resources.
  • Simulation results validated the model's efficiency in resource utilization and latency reduction.

Conclusions:

  • The intelligent task dispatching model effectively addresses the challenges of AI tasks in dynamic MEC environments.
  • The DQN algorithm provides an efficient approach for optimizing resource allocation and task scheduling in edge computing.
  • The proposed model offers a significant improvement over traditional task dispatching methods for intelligent IoT applications.