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An Enhanced Binary Particle Swarm Optimization (E-BPSO) algorithm for service placement in hybrid cloud platforms

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Abstract

Hybrid cloud platforms offer an attractive solution to organizations interested in implementing integrated private and public cloud applications to meet their profitability requirements. However, this can only be achieved by utilizing available resources while speeding up execution processes. Accordingly, deploying new applications entails dedicating some of these processes to a private cloud while allocating others to the public cloud. In this context, the current work aims to minimize relevant costs and deliver effective choices for an optimal service placement solution within minimal execution time. To date, several evolutionary algorithms have been applied to solve the challenging service placement problem by dealing with complex solution spaces to provide an optimal placement with relatively short execution times. In particular, the standard BPSO algorithm has been found to display a significant disadvantage, namely getting trapped in local optima and demonstrating a noticeable lack of robustness in dealing with service placement problems. Hence, to overcome the critical shortcomings associated with the standard BPSO, an enhanced binary particle swarm optimization (E-BPSO) algorithm is proposed, comprising a modification inspired by the continuous PSO for the particle position updating equation. Our proposed E-BPSO algorithm is shown to outperform state-of-the-art approaches using a real benchmark task in terms of both cost and execution time.

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Acknowledgements

The present research, contributing to achieving the highlighted promising results, has received a funding grant from the Ministry of Higher Education and Scientific Research of Tunisia under grant agreement number LR11ES48. We deeply acknowledge Taif University for supporting this study through Taif University Researchers Supporting Project number (TURSP-2020/327), Taif University, Taif, Saudi Arabia. The authors would like to thank the anonymous reviewers for their insightful comments and suggestions which helped improve the quality of the paper. Hussain acknowledges the support of the U.K. Engineering and Physical Sciences Research Council (EPSRC—Grants Ref. EP/M026981/1, EP/T021063/1, and Grant EP/T024917/1.

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Authors and Affiliations

  1. ISITCom, University of Sousse, 4011, Sousse, Tunisia

    Wissem Abbes

  2. REGIM-Lab.: Research Groups on Intelligent Machines, National Engineering School of Sfax (ENIS), University of Sfax, BP 1173, 3038, Sfax, Tunisia

    Wissem Abbes, Zied Kechaou & Adel M. Alimi

  3. School of Computing, Edinburgh Napier University, Edinburgh, UK

    Amir Hussain

  4. Department of Computer Science, College of Computers and Information Technology, Taif University, P.O. Box. 11099, Taif, 21944, Saudi Arabia

    Abdulrahman M. Qahtani

  5. College of Applied Computer Science, King Saud University, Al Muzahmiya Campus, Riyadh, Saudi Arabia

    Omar Almutiry & Habib Dhahri

  6. Department of Electrical and Electronic Engineering Science, Faculty of Engineering and the Built Environment, University of Johannesburg, Johannesburg, South Africa

    Adel M. Alimi

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  1. Wissem Abbes
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Abbes, W., Kechaou, Z., Hussain, A. et al. An Enhanced Binary Particle Swarm Optimization (E-BPSO) algorithm for service placement in hybrid cloud platforms. Neural Comput & Applic 35, 1343–1361 (2023). https://doi.org/10.1007/s00521-022-07839-5

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