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An enhanced whale optimization algorithm for task scheduling in edge computing environments.

Li Han1, Shuaijie Zhu1, Haoyang Zhao1

  • 1College of Computer Science and Technology, Zhengzhou University of Light Industry, Zhengzhou, China.

Frontiers in Big Data
|November 14, 2024
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Summary
This summary is machine-generated.

An enhanced whale optimization algorithm (EWOA) optimizes task scheduling in edge computing, reducing costs and completion time while improving resource utilization for demanding applications.

Keywords:
edge computingmulti-objective optimizationoptimization in edge computingtask schedulingwhale optimization algorithm

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Distributed Computing

Background:

  • Mobile devices and compute-intensive applications generate vast data, straining edge computing resources.
  • Real-time task execution in edge environments faces challenges due to limited resources and demanding applications.

Purpose of the Study:

  • To propose an enhanced whale optimization algorithm (EWOA) for efficient task scheduling in edge computing.
  • To develop a multi-objective model optimizing CPU, memory, time, and resource utilization.

Main Methods:

  • Developed a multi-objective task scheduling model for edge computing.
  • Transformed the model into a whale optimization problem, using chaotic mapping for population initialization and a nonlinear convergence factor for search balance.
  • Evaluated EWOA performance against ODTS, WOA, HWACO, and CATSA in an experimental edge computing environment.

Main Results:

  • EWOA reduced operational costs by 29.22%.
  • EWOA decreased task completion time by 17.04%.
  • EWOA improved node resource utilization by 9.5%.

Conclusions:

  • EWOA demonstrates significant improvements in cost, time, and resource utilization for edge computing task scheduling.
  • Limitations include not accounting for network delays and user mobility.
  • Future work will explore fault-tolerant scheduling for dynamic user needs and enhanced service quality.