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Fault-Tolerant Scheduling Mechanism for Dynamic Edge Computing Scenarios Based on Graph Reinforcement Learning

  • 0College of Computer Science and Technology, National University of Defense Technology, Changsha 410073, China.
Sensors (basel, Switzerland) +

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November 9, 2024

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November 9, 2024

View abstract on PubMed

Summary

This summary is machine-generated.

This study introduces a novel fault-tolerant scheduling model using asynchronous graph reinforcement learning for edge computing. The model ensures system resilience against node failures and dynamic resource changes, improving quality of service.

Area Of Science

  • Computer Science
  • Artificial Intelligence
  • Distributed Systems

Background

  • Edge computing handles real-time processing for Internet of Things (IoT) devices.
  • Edge environments face challenges like network instability and dynamic resource variations.
  • These challenges can cause task interruptions and system failures.

Purpose Of The Study

  • To develop a fault-tolerant scheduling mechanism for edge computing environments.
  • To ensure continuous system operation despite node failures.
  • To enhance the robustness of edge computing systems.

Main Methods

  • Proposed an innovative fault-tolerant scheduling model based on asynchronous graph reinforcement learning.
  • Utilized a deep reinforcement learning framework with a graph neural network to model node communication.
  • Implemented an asynchronous model update strategy for real-time dynamic scheduling.

Main Results

  • The proposed model accurately captured complex communication relationships between computing nodes.
  • The model generated effective fault-tolerant scheduling actions for dynamic environments.
  • Experimental results showed superior performance compared to baseline algorithms.

Conclusions

  • The asynchronous graph reinforcement learning model enhances fault-tolerant scheduling in edge computing.
  • The method significantly improved quality of service (QoS) assurance.
  • The approach offers a robust solution for dynamic and unstable edge environments.

Keywords:

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