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Task Pattern Identification and Scheduling Using Equal Opportunity Model for Minimization of Makespan and Task Diversity in Cloud Computing

  • PATTERN RECOGNITION AND IMAGE ANALYSIS AUTOMATED SYSTEMS, HARDWARE AND SOFTWARE
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Abstract

This article focuses on clustering tasks, normalization of multiple parameters under consideration and applying normalized adaptive league championship algorithm (NALCA) for minimization of makespan of tasks. Minimization of processing time and optimum resource utilization are fundamental requirements of any computing service provider. Scheduling performs crucial role in managing processing capabilities and resource utilization. Clustering is applied so that tasks are divided into various clusters and then scheduling is applied. In order to make comparative analysis of clustering, three clustering algorithms are implemented to have statistical details after applying each clustering algorithm. After experimentation it has been found out that hierarchical clustering with equal opportunity model with normalization LCA has gained outstanding performance enhancements.

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ACKNOWLEDGMENTS

The authors would like to express thanks to both the research supervisors Dr. M. Nirupama Bhat and Dr. Neeta Thakre for their time to guidance and motivation. Also, they express their gratitude towards Head of Computer Science and Engineering Department (VFSTR) Prof. Dr. Venkatesulu Dondeti for his support and cooperation.

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Authors and Affiliations

  1. Vignan’s Foundation for Science, Technology and Research (Deemed to be University), 522213, Vadlamudi, Guntur, Andhra Pradesh, India

    Anup Gade & M. Nirupama Bhat

  2. Priyadarshani College of Engineering, Priyadarshini Campus, Digdoh Hills, Hingna Road, 440019, Nagpur, Maharashtra, India

    Neeta Thakare

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  1. Anup Gade
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  2. M. Nirupama Bhat
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  3. Neeta Thakare
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Correspondence to Anup Gade, M. Nirupama Bhat or Neeta Thakare.

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COMPLIANCE WITH ETHICAL STANDARDS

This article is a completely original work of its authors; it has not been published before and will not be sent to other publications until the PRIA Editorial Board decides not to accept it for publication.

Conflict of Interest

The process of writing and the content of the article do not give grounds for raising the issue of a conflict of interest.

Additional information

Anup Gade is a research scholar in the Department of Computer Science and Engineering in Vignan’s Foundation for Science, Technology and Research (Deemed to be University), Vadlamudi, AP, India. He has completed his Master of Engineering (M.E.) in Computer Science (2014) from RGPV, Bhopal. His area of research is cloud computing and to mentioned precisely task scheduling in cloud computing. He is working from past 12 years in as an Assistant Professor. He has published more than 10 articles in the various journals.

M. Nirupama Bhat Mundukur is working as Professor in the Department of Computer Science and Engineering in Vignan’s Foundation for Science, Technology and Research (Deemed to be University), Vadlamudi, AP, India. She completed her PhD in Computer Science (2015) from Sri Padmavati Mahila ViswaVidyalayam, Tirupati, India. She has more than 20 years of academic experience and over 10 years of research. She published over 25 research papers in various National and International Journals and Conferences. Currently, she is working in the areas of cryptography, cloud computing, and data analytics.

Neeta Thakare is working as an Associate Professor at Piryadarshani College of Engineering, Nagpur, MS, India. She has completed her PhD in Computer Science and Engineering from Sant Gadge Baba Amravati University, Amravati. She has 24 years of experience in the field of academics. She has published more than 15 research articles in journal of national and international repute. She has authored 1 book titled as Step by Step: Object Oriented Programming using C++. Her book on data structure: An Algorithmic Approach and Implementation Using Python is in process of publication.

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Anup Gade, Bhat, M.N. & Thakare, N. Task Pattern Identification and Scheduling Using Equal Opportunity Model for Minimization of Makespan and Task Diversity in Cloud Computing. Pattern Recognit. Image Anal. 32, 67–77 (2022). https://doi.org/10.1134/S1054661821040088

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