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Exploring swarm intelligence optimization techniques for task scheduling in cloud computing: algorithms, performance analysis, and future prospects

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Abstract

The advent of the cloud computing paradigm has enabled innumerable organizations to seamlessly migrate, compute, and host their applications within the cloud environment, affording them facile access to a broad spectrum of services with minimal exertion. A proficient and adaptable task scheduler is essential to manage simultaneous user requests for diverse cloud services using various heterogeneous and varied resources. Inadequate scheduling may result in issues related to either under-utilization or over-utilization of resources, potentially causing a waste of cloud resources or a decline in service performance. Swarm intelligence meta-heuristics optimization technique has evinced conspicuous efficacy in tackling the intricacies of scheduling difficulties. Thus, the present manuscript seeks to undertake an exhaustive review of swarm intelligence optimization techniques deployed in the task-scheduling domain within cloud computing. This paper examines various swarm-based algorithms, investigates their application to task scheduling in cloud environments, and provides a comparative analysis of the discussed algorithms based on various performance metrics. This study also compares different simulation tools for these algorithms, highlighting challenges and proposing potential future research directions in this field. This review paper aims to shed light on the state-of-the-art swarm-based algorithms for task scheduling in cloud computing, showing their potential to improve resource allocation, enhance system performance, and efficiently utilize cloud resources.

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Data availability

The datasets generated during the current study are available from the corresponding author upon reasonable request.

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  1. Department of Information and Communication Engineering, Noakhali Science and Technology University, Noakhali, 3814, Bangladesh

    Farida Siddiqi Prity, K. M. Aslam Uddin & Nishu Nath

  2. Gianforte School of Computing, Montana State University, Bozeman, MT, USA

    Nishu Nath

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FSP: writing original draft, literature surveys, writing—review and editing; KMAU: study conception, supervision, and investigation on challenges; NN: draft manuscript preparation, writing—review and editing.

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Prity, F.S., Uddin, K.M.A. & Nath, N. Exploring swarm intelligence optimization techniques for task scheduling in cloud computing: algorithms, performance analysis, and future prospects. Iran J Comput Sci 7, 337–358 (2024). https://doi.org/10.1007/s42044-023-00163-8

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