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A hybrid GA-PSO strategy for computing task offloading towards MES scenarios.

Wenzao Li1,2, Xiulan Sun1, Bing Wan3

  • 1College of Communication Engineering, Chengdu University of Information Technology, Chengdu, China.

Peerj. Computer Science
|June 22, 2023
PubMed
Summary

This study introduces a task offloading strategy for mobile edge computing (MEC) with sparse server deployment. The proposed hybrid GA-PSO algorithm significantly improves workload balancing, reducing server load deviation by 90%.

Keywords:
Average transmission delayLoad balancingMobile edge computingTask offloading

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Area of Science:

  • Computer Science
  • Network Engineering
  • Distributed Systems

Background:

  • Mobile Edge Computing (MEC) offers solutions for delay-sensitive applications by enabling computation offloading.
  • Sparse deployment of Mobile Edge Servers (MES) due to cost constraints presents challenges for optimal performance.
  • Task offloading in MEC involves complex decisions regarding Access Point (AP) and MES selection, impacting transmission and processing delays.

Purpose of the Study:

  • To propose an effective task offloading strategy for MEC under sparse MES deployment scenarios.
  • To address the trade-off between system load balancing and task completion delay.
  • To optimize performance by minimizing transmission delays caused by system load and multi-hop transmissions.

Main Methods:

  • Formulation of a multi-objective optimization problem considering workload balancing and task completion delay.
  • Representation of the MEC system as an undirected and unweighted graph.
  • Development and application of a hybrid Genetic Algorithm-Particle Swarm Optimization (GA-PSO) algorithm with two-dimensional genes.

Main Results:

  • The hybrid GA-PSO algorithm demonstrated superior performance in workload balancing, achieving approximately 90% lower standard deviation compared to GA and NSA algorithms.
  • While not outperforming state-of-the-art algorithms in all task offloading delays, the proposed method significantly enhances load balancing.
  • The simulation results validate the effectiveness of the GA-PSO algorithm in optimizing MEC performance under sparse server conditions.

Conclusions:

  • The hybrid GA-PSO algorithm is an effective strategy for task offloading in MEC, particularly in scenarios with sparse MES density.
  • The algorithm successfully optimizes workload balancing, a critical factor for efficient MEC operation.
  • This approach provides a valuable solution for improving the performance and resource utilization of MEC systems.