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Multi objective task scheduling algorithm in cloud computing using grey wolf optimization

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Abstract

The scheduling of applications is one of the prominent challenges in cloud computing, due to run time mapping by task scheduler between upcoming workload and cloud resources. An efficient scheduling algorithm is needed to schedule the diverse workload and improve the performance metrics by minimizing makespan and maximizing resource utilization. Many of the existing scheduling techniques addressed only makespan and resource utilization parameters and did not consider some other significant parameters like Energy consumption, migration time etc. that directly impacts the performance of cloud services. To overcome the mentioned issues, authors have proposed a nature inspired multi-objective task scheduling Grey wolf optimization (MOTSGWO) algorithm that has the ability to take the scheduling decision at runtime based upon the status of cloud resources and upcoming workload demands. In addition, the proposed technique allocates the resources based upon the budget of end users as well as priorities of tasks. The proposed MOTSGWO approach implemented on Cloudsim toolkit and the workload is generated by creation of datasets (da01, da02, da03, da04) with different distributions of tasks and workload traces taken from HPC2N and NASA (da05, da06) parallel workload archives. The results of extensive experiment shows that the proposed MOTSGWO approach outperforms other baseline policies and improved the significant parameters.

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Authors and Affiliations

  1. School of Computer Science and Engineering, VIT-AP University, Amaravati, AP, India

    Sudheer Mangalampalli & Ganesh Reddy Karri

  2. Department of Information Technology, NIT Jalandhar, Jalandhar, India

    Mohit Kumar

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  1. Sudheer Mangalampalli
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  2. Ganesh Reddy Karri
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  3. Mohit Kumar
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Correspondence to Sudheer Mangalampalli.

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Mangalampalli, S., Karri, G.R. & Kumar, M. Multi objective task scheduling algorithm in cloud computing using grey wolf optimization. Cluster Comput 26, 3803–3822 (2023). https://doi.org/10.1007/s10586-022-03786-x

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