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Holding maximum customers in cloud business environment by efficient load balancing methods based on MPSO-MC

Author

Listed:
  • P. Sundaramoorthy

    (SNS College of Technology)

  • M. Selvam

    (Excel Engineering College)

  • S. Karthik

    (SNS College of Technology)

  • K. Srihari

    (SNS College of Engineering)

Abstract

As is well-known Cloud is an Environment for sharing resources based on Anything as a Service (XaaS) pattern that includes software, platform, infrastructure, storage, etc. on demand. For allocating resources and managing it efficiently, the load has to be balanced on the cloud paradigm. Moreover, the reliable resource allocation with load balancing has become the significant resource focus in the current scenario. In the heterogeneous cloud environment, dispersion and uncertainty of cloud resources faces issues on the process of allocation that are not effectively handled and accessed by the existing approaches. With that concern, for providing proficient resource scheduling with apposite load balancing, an efficient load-balancing model based on modified particle swarm optimization with membrane computing has been proposed. Based on that, suitable resources are allocated for different jobs in accordance with the factors like completion time, scalability, makespan, utilization of resources, reliability, availability, etc. Moreover, in this paper, effective resource scheduling has been achieved with the modified particle swarm optimization that combined with membrane computing local and glob optimization of inter-membranes for providing an optimal solution. Spatial segmentation has also been performed for enhancing the membrane-based optimization.

Suggested Citation

  • P. Sundaramoorthy & M. Selvam & S. Karthik & K. Srihari, 2020. "Holding maximum customers in cloud business environment by efficient load balancing methods based on MPSO-MC," Information Systems and e-Business Management, Springer, vol. 18(3), pages 295-309, September.
    • Handle: RePEc:spr:infsem:v:18:y:2020:i:3:d:10.1007_s10257-019-00413-y
    DOI: 10.1007/s10257-019-00413-y
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    References listed on IDEAS

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    1. Bendraouche, Mohamed & Boudhar, Mourad & Oulamara, Ammar, 2015. "Scheduling: Agreement graph vs resource constraints," European Journal of Operational Research, Elsevier, vol. 240(2), pages 355-360.
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