Resource Optimization in Cloud Data Centers Using Particle Swarm Optimization

Madhumala R. B., Harshvardhan Tiwari and Devaraj Verma C.
Additional contact information
Madhumala R. B.: Jain University (Deemed), India
Harshvardhan Tiwari: Jyothy Institute of Technology, India
Devaraj Verma C.: Jain University (Deemed), India

International Journal of Cloud Applications and Computing (IJCAC), 2022, vol. 12, issue 2, 1-12

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.

Date: 2022
References: Add references at CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/IJCAC.305856 (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:igg:jcac00:v:12:y:2022:i:2:p:1-12

Access Statistics for this article

International Journal of Cloud Applications and Computing (IJCAC) is currently edited by B. B. Gupta

More articles in International Journal of Cloud Applications and Computing (IJCAC) from IGI Global
Bibliographic data for series maintained by Journal Editor ().