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Minimum-Cost Offloading for Collaborative Task Execution of MEC-Assisted Platooning.

Xiayan Fan1,2, Taiping Cui3,4, Chunyan Cao5,6

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This study introduces a method for optimizing task offloading in vehicle platoons collaborating with Mobile Edge Computing (MEC) servers. The approach significantly cuts task execution costs and ensures deadlines are met through smart scheduling.

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Area of Science:

  • Computer Science
  • Mobile Computing
  • Edge Computing

Background:

  • Collaborative task execution in vehicle platoons presents challenges in resource allocation and meeting task deadlines.
  • Mobile Edge Computing (MEC) offers a potential solution for offloading computational tasks from vehicles.

Purpose of the Study:

  • To develop an optimal offloading decision strategy for collaborative task execution between vehicle platoons and MEC servers.
  • To minimize the cost of task offloading while ensuring all tasks are completed within their deadlines.

Main Methods:

  • Tasks are modeled as a series of fine-grained tasks with a linear topology.
  • The offloading decision problem is transformed into a shortest path problem on a directed acyclic graph.
  • The Lagrangian Relaxation-based Aggregated Cost (LARAC) algorithm is employed for approximate solution.

Main Results:

  • The proposed task scheduling method is effective for platoon scenarios involving MEC cooperation.
  • Optimal offloading decisions significantly reduce task execution costs compared to local, platoon, or MEC-only execution.
  • The strategy successfully meets task execution deadlines.

Conclusions:

  • Collaborative task execution between platoons and MEC servers, guided by the LARAC algorithm, is a viable and efficient approach.
  • The developed offloading strategy provides substantial cost savings and deadline adherence in mobile computing environments.