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A multiobjective optimization of task workflow scheduling using hybridization of PSO and WOA algorithms in cloud-fog computing

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Abstract

Workflow scheduling poses a significant challenge due to the large scale of workflows and heterogeneity of algorithms in cloud-fog environment. Also, these nature-inspired algorithms have limitations, such as being trapped in local optima, leading to failure in achieving a global optimal solution. Since, workflow scheduling is used to enhance the optimization of task allocation and execution within cloud-fog computing. Therefore, various metaheuristic algorithms have been recommended by researchers for addressing scheduling problems. Our paper used a hybrid algorithm “particle swarm optimization-whale optimization algorithm” (PSO-WOA) to overcome the abovementioned issues. When applied individually, the PSO becomes trapped in the global optimum due to the presence of a high-dimensional search space, while the WOA, which lacks flexibility in search spacing, becomes trapped in local optima. The proposed PSO-WOA algorithm overcomes the constrained exploration of search spaces observed in the population-based WOA algorithm and efficiently mitigates the challenge of becoming trapped in local optima. The simulation was performed using the WorkflowSim toolkit, and the results were tested for several test cases on five scientific workflows with varying numbers of tasks and iterations. The results illustrate that the hybrid PSO-WOA approach produced superior outcomes when compared with state-of-the-art algorithms (ACO, PSO, WOA, GA and BH-FWA) for both total execution cost (TEC) and total execution time (TET). After analysis of results, two workflows i.e., montage and epigenomics stood apart from others in terms of time and cost. Statistical analysis was done with Wilcoxon and Friedman tests and were found to be significant. In summary, this study provides a comparative analysis of enhancing both TET and TEC in a hybrid algorithm along with its impact on performance metrics.

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  1. Department of Computer Science and Engineering, Punjabi University, Patiala, India, 147002

    Sumit Bansal & Himanshu Aggarwal

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  1. Sumit Bansal
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I, Sumit Bansal, was primarily responsible for the literature review, methodology and experimental work of this manuscript. Dr. Himanshu Aggarwal played a significant role in analyzing the results and editing the final manuscript.

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Correspondence to Sumit Bansal.

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Bansal, S., Aggarwal, H. A multiobjective optimization of task workflow scheduling using hybridization of PSO and WOA algorithms in cloud-fog computing. Cluster Comput (2024). https://doi.org/10.1007/s10586-024-04522-3

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