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Smart Job Scheduling Model for Cloud Computing Network Application

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Abstract

Task scheduling is one of the significant factors for heterogeneous type of elements in multi-cloud computing environment. It is to delegate activities to most adequate resources to raise the performance with respect to some dynamic parameters. The suggested model of scheduling was designed to run applications of cloud computing which applied in three steps (classifying, execution, minimization, and rating) and is assumed to be completion time, average waiting and turnaround time, and render duration as output parameters. The tasks’ execution time in cloud computing applications was created in the task scheduling model by exponential and normal distribution. The task ranking depends on the shortest strategy for First Job and the results are compared with other “Largest Processing Time” and “First Come First Serve” ranking method. Scheduling model proposed provides significant output than per performance parameters specified.

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No funding was received for this study.

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Authors and Affiliations

  1. Department of Vocational and Technical Education, Faculty of Education, Alex Ekwueme Federal University, Ndufu-Alike, Abakaliki, Nigeria

    Edeh Michael Onyema

  2. Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Chennai, 602105, India

    Edeh Michael Onyema

  3. GP Technologies LLC, Texas, USA

    Venkataramaiah Gude

  4. Department of Computer Science and Engineering, Shivalik College of Engineering, Dehradun, India

    Ashutosh Bhatt

  5. School of Computer Science, University of Petroleum and Energy Studies, Dehradun, India

    Ambika Aggarwal & Sunil Kumar

  6. Department of Computer Science, Federal University of Technology, Owerri, Nigeria

    Mercy Eberechi Benson-Emenike

  7. Department of Computer Engineering, Enugu State University of Science and Technology, Enugu, Nigeria

    Lois Onyejere Nwobodo

Authors
  1. Edeh Michael Onyema
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  2. Venkataramaiah Gude
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  3. Ashutosh Bhatt
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  4. Ambika Aggarwal
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  5. Sunil Kumar
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  6. Mercy Eberechi Benson-Emenike
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  7. Lois Onyejere Nwobodo
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Contributions

EMO—conception and design of study, AB—acquisition of data, AA—analysis and interpretation of data; SK—drafting; VG—formalization an editing, MEB-E—review and investigation, MEB-E—conceptualization, LON investigation and analysis.

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Correspondence to Edeh Michael Onyema.

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This article is part of the topical collection “Machine Intelligence and Smart Systems” guest edited by Manish Gupta and Shikha Agrawal.

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Onyema, E.M., Gude, V., Bhatt, A. et al. Smart Job Scheduling Model for Cloud Computing Network Application. SN COMPUT. SCI. 5, 39 (2024). https://doi.org/10.1007/s42979-023-02441-5

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