RETRACTED ARTICLE: A meta-heuristic multiple ensemble load balancing framework for real-time multi-task cloud scheduling process

Gutta Sridevi () and Midhun Chakkravarthy
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Gutta Sridevi: Lincoln University College
Midhun Chakkravarthy: Lincoln University College

International Journal of System Assurance Engineering and Management, 2021, vol. 12, issue 6, No 30, 1459-1476

Abstract: 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.

Keywords: Ensemble load balancing; Cloud servers; Meta-heuristic; Task scheduling (search for similar items in EconPapers)
Date: 2021
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DOI: 10.1007/s13198-021-01244-2

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