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Ant Colony Optimization Task Scheduling Algorithm for SWIM Based on Load Balancing

Author

Listed:
  • Gang Li

    (School of Electrical and Information Engineering, Tianjin University, Tianjin 300072, China
    School of Mechanical Engineering, Baicheng Normal University, Baicheng 137000, China)

  • Zhijun Wu

    (School of Electronic Information and Automation, Civil Aviation University of China, Tianjin 300300, China)

Abstract

This paper focuses on the load imbalance problem in System Wide Information Management (SWIM) task scheduling. In order to meet the quality requirements of users for task completion, we studied large-scale network information system task scheduling methods. Combined with the traditional ant colony optimization (ACO) algorithm, using the hardware performance quality index and load standard deviation function of SWIM resource nodes to update the pheromone, a SWIM ant colony task scheduling algorithm based on load balancing (ACTS-LB) is presented in this paper. The experimental simulation results show that the ACTS-LB algorithm performance is better than the traditional min-min algorithm, ACO algorithm and particle swarm optimization (PSO) algorithm. It not only reduces the task execution time and improves the utilization of system resources, but also can maintain SWIM in a more load balanced state.

Suggested Citation

  • Gang Li & Zhijun Wu, 2019. "Ant Colony Optimization Task Scheduling Algorithm for SWIM Based on Load Balancing," Future Internet, MDPI, vol. 11(4), pages 1-18, April.
    • Handle: RePEc:gam:jftint:v:11:y:2019:i:4:p:90-:d:219332
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    Cited by:

    1. Amit Chhabra & Sudip Kumar Sahana & Nor Samsiah Sani & Ali Mohammadzadeh & Hasmila Amirah Omar, 2022. "Energy-Aware Bag-of-Tasks Scheduling in the Cloud Computing System Using Hybrid Oppositional Differential Evolution-Enabled Whale Optimization Algorithm," Energies, MDPI, vol. 15(13), pages 1-36, June.

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