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A task clustering based QoS aware scheduling algorithm for task execution in cloud-Iot model for education services

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Abstract

Some of the conventional approaches to task scheduling are discussed in this section. The scheduling of tasks in the Internet of Things (IoT) application is a complex job in cloud computing due to the heterogeneity characteristics of the IoT. Therefore, an efficient scheduling approach is vital in improving the performance of the Cloud-IoT applications. The improvement in QoS mainly depends on efficient task scheduling and allocating the task to the appropriate Virtual Machine (VM). This paper uses a clustering-based approach to design an optimal solution for task scheduling and VM allocation for IoT applications in education services. The workflow is simplified with horizontal and vertical clustering, and VM is allocated. The task is classified into coarse and fine-grained, based on the average execution time of the total number of tasks. The average execution time is calculated based on the best and worst performance of VM. The vertical clustering approach clusters the tasks into sequential and parallel, thereby turning the huge volume of tasks into simple ones. The horizontal clustering approach selects the VM with the help of a bipartite matching graph which enhances the Quality of Service (QoS). The proposed task clustering has been evaluated in metrics like makespan, response time, throughput, total execution time, task completion time, and VM utilization. The proposed approach achieved a makespan of 2500 ms and throughput of 83 Mbps for 100 tasks, a response time of 0.12 ms, a total execution time of 640 ms, and a total task completion time of 340,000 ms for a 2 arrival rate based on performance evaluation results.

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Authors and Affiliations

  1. Faculty of Computer Science and Engineering, Dr. A P J Abdul Kalam Technical University, Lucknow, Uttar Pradesh, India

    Sunil Kumar Chowdhary

  2. Department of Computer Science and Engineering, G.L. Bajaj Institute of Technology and Management, Noida, India

    A. L. N. Rao

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  1. Sunil Kumar Chowdhary
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  2. A. L. N. Rao
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Contributions

All the authors have participated in writing the manuscript and have revised the final version. All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by Sunil Kumar Chowdhary, A L N Rao. The first draft of the manuscript was written by Sunil Kumar Chowdhary and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript. Conceptualization: Sunil Kumar Chowdhary; Methodology: Sunil Kumar Chowdhary, A L N Rao; Formal analysis and investigation: A L N Rao; Writing - original draft preparation: Sunil Kumar Chowdhary; Writing - review and editing: Sunil Kumar Chowdhary, A L N Rao; Supervision: A L N Rao.

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Correspondence to Sunil Kumar Chowdhary.

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Chowdhary, S.K., Rao, A.L.N. A task clustering based QoS aware scheduling algorithm for task execution in cloud-Iot model for education services. Multimed Tools Appl 82, 44783–44800 (2023). https://doi.org/10.1007/s11042-023-15392-z

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  • DOI: https://doi.org/10.1007/s11042-023-15392-z

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