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An improved particle swarm optimization algorithm for task scheduling in cloud computing

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Abstract

In the context of cloud computing, the task scheduling issue has an immediate effect on service quality. Task scheduling is the process of assigning work to available resources based on requirements. The objective of this NP-hard problem is to identify the ideal timetable for resource allocation so that more tasks can be done in less time. Several algorithms have been presented thus far to solve the problem of work scheduling. In this paper proposes an Improved Particle Swarm Optimization (IPSO) algorithm to address the aforementioned issue. In order to shorten the execution time of the original Particle Swarm Optimization (PSO) algorithm for task scheduling in the cloud computing environment, a multi-adaptive learning strategy is employed. In its initial population phase, the proposed Multi Adaptive Learning for Particle Swarm Optimization (MALPSO) defines two sorts of particles: ordinary particles and locally best particles. During this phase, the population's variety is reduced and the likelihood of reaching the local optimum rises. This study compares the proposed approach to various algorithms based on four criteria: makespan, load balancing, stability, and efficiency. Additionally, we examine the proposed technique using the CEC 2017 benchmark. Compared to what is currently known, the suggested method can solve the problem in less time and get the best answer for most of the criteria.

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Authors and Affiliations

  1. Hebei Key Laboratory of Machine Learning and Computational Intelligence, Hebei University, Baoding, 071002, China

    Poria Pirozmand & Yingqiu Li

  2. Department of Mathematics and Computer Science, Bandar Abbas Branch, Islamic Azad University, Bandar Abbas, Iran

    Hoda Jalalinejad

  3. Department of Computer Science, University of Regina, Regina, Canada

    Ali Asghar Rahmani Hosseinabadi

  4. Department of Computer Engineering and IT, Payame Noor University (PNU), Tehran, Iran

    Seyedsaeid Mirkamali

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  1. Poria Pirozmand
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  3. Ali Asghar Rahmani Hosseinabadi
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  4. Seyedsaeid Mirkamali
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Correspondence to Ali Asghar Rahmani Hosseinabadi.

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Pirozmand, P., Jalalinejad, H., Hosseinabadi, A.A.R. et al. An improved particle swarm optimization algorithm for task scheduling in cloud computing. J Ambient Intell Human Comput 14, 4313–4327 (2023). https://doi.org/10.1007/s12652-023-04541-9

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  • DOI: https://doi.org/10.1007/s12652-023-04541-9

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