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An optimized algorithm for resource utilization in cloud computing based on the hybridization of meta-heuristic algorithms

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Abstract

Cloud services are rapidly growing and in high demand. Load Balancing (L.B.) is crucial for optimizing the workload among Virtual Machines (V.M.s) to utilize resources effectively. This study proposes a hybrid approach called Grey Wolf and Particle Swarm Optimization (GW-PSO) to address task scheduling issues and provide optimal L.B. to all V.M.s. The primary objective is identifying the optimized V.M.s using the proposed hybrid methodology. Additionally, parallel task scheduling minimizes response time and provides quick results for each task. The study utilizes Grey Wolf (G.W.) for parallel task scheduling and Particle Swarm Optimization (PSO) to obtain the optimal solution based on G.W., thereby identifying the optimized V.M.s. This approach ensures flexibility among V.M.s, preventing them from overloading or underloading. All V.M.s are equally assigned tasks. The proposed G.W. calculates the Fitness Value (F.V.) and saves it, which is then passed to PSO. The best particle is updated with its position and velocity, helping identify the optimized V.M.s and assigning loads based on the obtained optimal solution. Performance analysis considers essential parameters such as average Load, processor utilization, turnaround time, response time, runtime, and memory utilization. The analytical results demonstrate the effectiveness of the proposed method compared to the existing system in terms of these parameters.

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Data availability

The data that support the findings of this study are available from the corresponding author upon reasonable request.

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Authors and Affiliations

  1. Faculty of Sciences, D/O Computer Science, Aligarh Muslim University, Aligarh, Uttar Pradesh, India

    Fakhrun Jamal & Tamanna Siddiqui

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  1. Fakhrun Jamal
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  2. Tamanna Siddiqui
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Correspondence to Fakhrun Jamal.

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Jamal, F., Siddiqui, T. An optimized algorithm for resource utilization in cloud computing based on the hybridization of meta-heuristic algorithms. Int. j. inf. tecnol. (2023). https://doi.org/10.1007/s41870-023-01549-4

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