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RETRACTED ARTICLE: A meta-heuristic multiple ensemble load balancing framework for real-time multi-task cloud scheduling process

Author

Listed:
  • Gutta Sridevi

    (Lincoln University College)

  • Midhun Chakkravarthy

    (Lincoln University College)

Abstract

Meta heuristic algorithms play a key role in the load balancing applications due to high computing efficiency and runtime. As the number of tasks and load balancing resources are increasing in size and dimensions, the efficiency of the scalable load balancing metrics and computing power gradually decreases in commercial cloud storage servers. In this paper, a hybrid meta-heuristic-based load balancing framework is designed and implemented in the real-time cloud environment. In this framework, a multiple load balancer is implemented to take decision on the task allocation and resources optimization. In this work, an advanced particle swarm optimization (IPSO) and improved ant colony optimization are used for majority voting process in the cloud computing environment. In this model, hybrid optimization constraints are evaluated to find the optimization in the task scheduling process. Experimental results show that the present ensemble load balancing model has better efficiency in real-time task scheduling process than the conventional models.

Suggested Citation

  • Gutta Sridevi & Midhun Chakkravarthy, 2021. "RETRACTED ARTICLE: A meta-heuristic multiple ensemble load balancing framework for real-time multi-task cloud scheduling process," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 12(6), pages 1459-1476, December.
    • Handle: RePEc:spr:ijsaem:v:12:y:2021:i:6:d:10.1007_s13198-021-01244-2
    DOI: 10.1007/s13198-021-01244-2
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    References listed on IDEAS

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    1. Hosseini, Farzad & Safari, Amin & Farrokhifar, Meisam, 2020. "Cloud theory-based multi-objective feeder reconfiguration problem considering wind power uncertainty," Renewable Energy, Elsevier, vol. 161(C), pages 1130-1139.
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