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Towards Application-Driven Task Offloading in Edge Computing Based on Deep Reinforcement Learning.

Ming Sun1,2, Tie Bao1, Dan Xie1

  • 1College of Computer Science and Technology, Jilin University, Changchun 130012, China.

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Summary
This summary is machine-generated.

This study introduces a novel strategy for edge computing task offloading, optimizing application performance and energy use. The proposed method significantly reduces total costs for edge computing services.

Keywords:
application-driven taskdeep reinforcement learningedge computingtask offloading

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Area of Science:

  • Edge Computing
  • Distributed Systems
  • Artificial Intelligence

Background:

  • Edge computing offers distributed resources between cloud data centers and end devices.
  • Application-driven task offloading in edge computing presents challenges due to inter-task dependencies.
  • Optimizing for delay and energy while ensuring Quality of Service (QoS) is critical.

Purpose of the Study:

  • To address the application-driven task offloading problem in multi-user edge computing environments.
  • To jointly optimize application delay and energy consumption while maintaining QoS.
  • To develop an efficient strategy for managing dependent sub-tasks in edge computing.

Main Methods:

  • Formulation of the edge computing task offloading problem, considering delays and energy consumption.
  • Proposal of a novel Application-driven Task Offloading Strategy (ATOS) utilizing deep reinforcement learning.
  • Development of a heuristic algorithm with a new factor for ordering parallel sub-tasks.

Main Results:

  • The ATOS strategy effectively optimizes task offloading for applications with dependent sub-tasks.
  • Experimental validation demonstrates the effectiveness and reliability of the proposed ATOS algorithm.
  • ATOS achieved an average total cost reduction of up to 64.5% compared to baseline strategies.

Conclusions:

  • The developed ATOS provides a robust solution for joint delay and energy optimization in edge computing.
  • Deep reinforcement learning combined with a heuristic sorting mechanism enhances task offloading efficiency.
  • The strategy ensures QoS while significantly reducing operational costs in edge environments.