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Resource Optimization in Cloud Data Centers Using Particle Swarm Optimization

Author

Listed:
  • Madhumala R. B.

    (Jain University (Deemed), India)

  • Harshvardhan Tiwari

    (Jyothy Institute of Technology, India)

  • Devaraj Verma C.

    (Jain University (Deemed), India)

Abstract

To meet the ever-growing demand for computational resources, it is mandatory to have the best resource allocation algorithm. In this paper, Particle Swarm Optimization (PSO) algorithm is used to address the resource optimization problem. Particle Swarm Optimization is suitable for continuous data optimization, to use in discrete data as in the case of Virtual Machine placement we need to fine-tune some of the parameters in Particle Swarm Optimization. The Virtual Machine placement problem is addressed by our proposed model called Improved Particle Swarm Optimization (IM-PSO), where the main aim is to maximize the utilization of resources in the cloud datacenter. The obtained results show that the proposed algorithm provides an optimized solution when compared to the existing algorithms.

Suggested Citation

  • Madhumala R. B. & Harshvardhan Tiwari & Devaraj Verma C., 2022. "Resource Optimization in Cloud Data Centers Using Particle Swarm Optimization," International Journal of Cloud Applications and Computing (IJCAC), IGI Global, vol. 12(2), pages 1-12, April.
    • Handle: RePEc:igg:jcac00:v:12:y:2022:i:2:p:1-12
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    File URL: http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/IJCAC.305856
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    Cited by:

    1. Zhou, Yufei & Wang, Sihan & Zhang, Nuo, 2023. "Dynamic decision-making analysis of Netflix's decision to not provide ad-supported subscriptions," Technological Forecasting and Social Change, Elsevier, vol. 187(C).

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