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Boosting white shark optimizer for global optimization and cloud scheduling problem

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Abstract

With the growing adoption of cloud computing in both public and private sector enterprises, the industry has experienced rapid expansion. To fully unlock the potential of cloud computing, efficient task scheduling becomes crucial. In cloud computing, task scheduling involves optimizing the allocation of tasks to a diverse range of resources, such as virtual machines, with the goals of reducing makespan, maximizing resource utilization, and minimizing response times. This challenge becomes even more pronounced for large-scale tasks due to the NP-hard nature of the problem. Consequently, the integration of metaheuristic algorithms into task scheduling has emerged as a solution to equitably distribute complex and diverse tasks across limited resources within acceptable timeframes. To enhance the quality of cloud computing services, this research introduces the modified white shark optimizer (mWSO) as an alternative task scheduling technique. The improved variant mWSO boosts the performance of the original WSO by introducing the following three enhancement steps: (1) introduce memory-based WSO to boost the exploitation phase, (2) propose an exploration-exploitation balance phase to enhance the exploration phase, and (3) introduce a control randomization parameter to balance exploration and exploitation properly. The mWSO is subjected to testing on both the global optimization problems from CEC2020 and cloud task scheduling problems. The experimental results of mWSO demonstrate high performance for CEC2020 competition benchmarks compared to other state-of-the-art and recent metaheuristic algorithms. In the case of the task scheduling problem, the mWSO achieved − 0.01 to 13.53% and 0.62–10.42% makespan and energy consumption reduction, respectively, for CEA-Curie workloads. For HPC2N workloads, mWSO achieved 7.27–29.53% makespan reduction and 3.52–26.08% energy savings over the compared metaheuristics. The statistical validity of the performance is also verified using Wilcoxon’s rank-sum test. The experimental results and comparison analysis reveal the consistent and better performance of the proposed mWSO to solve optimization problems.

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Authors and Affiliations

  1. Centre for Data Analytics and Cybersecurity (CDAC), Research Institute of Sciences and Engineering (RISE), University of Sharjah, Sharjah, 27272, United Arab Emirates

    Reham R. Mostafa

  2. Information Systems Department, Faculty of Computers and Information Sciences, Mansoura University, Mansoura, 35516, Egypt

    Reham R. Mostafa

  3. Department of Computer Engineering and Technology, Guru Nanak Dev University, Amritsar, 143005, India

    Amit Chhabra

  4. Computer Science Department, University of Sharjah, Sharjah, 27272, United Arab Emirates

    Ahmed M. Khedr

  5. Faculty of Engineering, Helwan University, Helwan, Egypt

    Fatma A. Hashim

  6. MEU Research Unit, Middle East University, Amman, 11831, Jordan

    Fatma A. Hashim

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  1. Reham R. Mostafa
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Correspondence to Ahmed M. Khedr.

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Mostafa, R.R., Chhabra, A., Khedr, A.M. et al. Boosting white shark optimizer for global optimization and cloud scheduling problem. Neural Comput & Applic (2024). https://doi.org/10.1007/s00521-024-09599-w

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