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Research Article

An Energy Efficient Particle Swarm Optimization based VM Allocation for Cloud Data Centre: EEVMPSO

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  • @ARTICLE{10.4108/eetsis.3254,
        author={Abhishek Kumar Pandey and Sarvpal Singh},
        title={An Energy Efficient Particle Swarm Optimization based VM Allocation for Cloud Data Centre: EEVMPSO},
        journal={EAI Endorsed Transactions on Scalable Information Systems},
        volume={10},
        number={5},
        publisher={EAI},
        journal_a={SIS},
        year={2023},
        month={8},
        keywords={Particle Swarm Optimization (PSO), Cloud computing, cloud data center, virtual machine placement, service level agreements},
        doi={10.4108/eetsis.3254}
    }
    
  • Abhishek Kumar Pandey
    Sarvpal Singh
    Year: 2023
    An Energy Efficient Particle Swarm Optimization based VM Allocation for Cloud Data Centre: EEVMPSO
    SIS
    EAI
    DOI: 10.4108/eetsis.3254
Abhishek Kumar Pandey1,*, Sarvpal Singh1
  • 1: Madan Mohan Malaviya University of Technology
*Contact email: akpsiet@gmail.com

Abstract

Virtual Machine (VM) allocation are the crucial problems because cloud computing enables the rapid growth of data centres and compute centres. Power consumption and network expenses have increased as cloud computing becomes more and more prevalent. System instability may result from repeated requests for computing resources. One of the most important and difficulties facing virtualization technology is finding the best way to stack virtual machines on top of physical machines in cloud data centres. The host must move virtual machines from overloaded to underloaded hosts as part of load balancing, which has an impact on energy consumption. The proposed energy efficient particle swarm optimization algorithm (EEVMPSO) for Virtual Machine allocation to maximize the load balancing. System resources including CPU, storage, and memory are optimized using EEVMPSO. This research article suggests energy-aware virtual machine migration using the Particle Swarm Optimization Algorithm for dynamic VMs placement, energy efficient cloud data centres as a solution to this issue. The experimental result shown in the proposed method, consumption energy in comparison to the PAPSO, KHA, EALBPSO, and RACC-MDT algorithm by 10.86%, 18.22%, 25.8%, and 31.34% respectively, it demonstrated the improvements in the energy service level agreements violation 5.77%, 15.3%, 26.19%, and 30.4%, as well as the average CPU utilization 2.2%, 24%, 22.6%, and 14.6%.  

Keywords
Particle Swarm Optimization (PSO), Cloud computing, cloud data center, virtual machine placement, service level agreements
Received
2023-04-15
Accepted
2023-07-30
Published
2023-08-01
Publisher
EAI
http://dx.doi.org/10.4108/eetsis.3254

Copyright © 2023 Pandey et al., licensed to EAI. This is an open access article distributed under the terms of the CC BY-NC-SA 4.0, which permits copying, redistributing, remixing, transformation, and building upon the material in any medium so long as the original work is properly cited.

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