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MPSO: A Novel Meta-Heuristics for Load Balancing in Cloud Computing

Author

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  • Subhadarshini Mohanty

    (Department of Computer Science and Engineering, Siksha ‘O' Anusandhan University, Bhubaneswar, India)

  • Prashanta Kumar Patra

    (Department of Computer Science and Engineering, College of Engineering and Technology Bhubaneswar, Bhubaneswar, India)

  • Subasish Mohapatra

    (Department of Computer Science and Application, College of Engineering and Technology Bhubaneswar, Bhubaneswar, India)

  • Mitrabinda Ray

    (Department of Computer Science and Engineering, Siksha ‘O' Anusandhan University, Bhubaneswar, India)

Abstract

Cloud computing is gaining more popularity due to its advantages over conventional computing. It offers utility based services to subscribers on demand basis. Cloud hosts a variety of web applications and provides services on the pay-per-use basis. As the users are increasing in the cloud system, the load balancing has become a critical issue. Scheduling workloads in the cloud environment among various nodes are essential to achieving a better Quality of Service (QOS). It is a prominent area of research as well as challenging to allocate the resources with changeable capacities and functionality. In this paper, a load balancing algorithm using Multi Particle Swarm Optimization (MPSO) has been developed by utilizing the benefits of particle swarm optimization (PSO) algorithm. Proposed approach aims to minimize the task overhead and maximize the resource utilization in a homogenous cloud environment. Performance comparisons are made with Genetic Algorithm (GA), Multi GA, PSO and other popular algorithms on different measures like makespan calculation and resource utilization.

Suggested Citation

  • Subhadarshini Mohanty & Prashanta Kumar Patra & Subasish Mohapatra & Mitrabinda Ray, 2017. "MPSO: A Novel Meta-Heuristics for Load Balancing in Cloud Computing," International Journal of Applied Evolutionary Computation (IJAEC), IGI Global, vol. 8(1), pages 1-25, January.
    • Handle: RePEc:igg:jaec00:v:8:y:2017:i:1:p:1-25
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