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A Chaos BSA-Based Optimization Approach for Task Planning to Improve Resource Deployment in Cloud Computing

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Abstract

In cloud computing, resource use and processing costs are closely tied to task scheduling. To ensure the best work completion, a variety of optimal task scheduling techniques make good use of these parameters. Task scheduling especially increases the cloud-based system’s source utilization and processing costs. In order to provide optimal scheduling, numerous optimization methodologies are used to enhance task scheduling performance. An analysis of a Chaos Bird Swarm Algorithm (Chaos BSA) is used to optimize job scheduling in a cloud environment in order to improve resource consumption. Processing, load, and source bandwidth are the three parameters of a multi-objective function on which the BSA is applied. Using chaos theory to expand the solution space’s exploration region, the BSA population is first created. The experiment is carried out using the proper tool, and simulation results have shown that, in contrast to GA and PSO techniques. For the purpose of optimizing job scheduling and improving resource efficiency in a cloud setting, the Chaos Bird Swarm Algorithm (Chaos BSA) is used. Due to the local optimal problem and initial parameter dependence of techniques, task scheduling algorithms may be improved to some extent. Here, a Chaos Bird Swarm Optimization (Chaos BSA) is used to create the best job scheduling scheme over a cloud environment. By fusing chaos behaviour with optimization, it improves the solution search space’s capacity for exploration and exploitation. The outcome is assessed in relation to the total cost, which takes into account sources, storage, and processing, as well as the number of jobs and iterations. For low number of tasks (500 tasks) based on the number of iterations, the PSO achieves 22% better results compared to GA, while Chaos BSA achieves 34% greater efficiency in terms of overall cost. Whereas, for 2000 tasks based on the number of iterations the PSO achieves 25% better results compared to GA, while Chaos BSA achieves 35% greater efficiency in terms of overall cost.

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  1. Chitkara University Institute of Engineering and Technology, Chitkara University, Chandigarh, Punjab, India

    Archana Mantri

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This article is part of the topical collection “Research Trends in Computational Intelligence” guest edited by Anshul Verma, Pradeepika Verma, Vivek Kumar Singh and S. Karthikeyan.

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Mantri, A. A Chaos BSA-Based Optimization Approach for Task Planning to Improve Resource Deployment in Cloud Computing. SN COMPUT. SCI. 4, 723 (2023). https://doi.org/10.1007/s42979-023-02179-0

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