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A High Performance Model for Task Allocation in Distributed Computing System Using K-Means Clustering Technique

A High Performance Model for Task Allocation in Distributed Computing System Using K-Means Clustering Technique

Harendra Kumar, Nutan Kumari Chauhan, Pradeep Kumar Yadav
Copyright: © 2021 |Pages: 25
ISBN13: 9781799853398|ISBN10: 179985339X|EISBN13: 9781799853404
DOI: 10.4018/978-1-7998-5339-8.ch060
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MLA

Kumar, Harendra, et al. "A High Performance Model for Task Allocation in Distributed Computing System Using K-Means Clustering Technique." Research Anthology on Architectures, Frameworks, and Integration Strategies for Distributed and Cloud Computing, edited by Information Resources Management Association, IGI Global, 2021, pp. 1244-1268. https://doi.org/10.4018/978-1-7998-5339-8.ch060

APA

Kumar, H., Chauhan, N. K., & Yadav, P. K. (2021). A High Performance Model for Task Allocation in Distributed Computing System Using K-Means Clustering Technique. In I. Management Association (Ed.), Research Anthology on Architectures, Frameworks, and Integration Strategies for Distributed and Cloud Computing (pp. 1244-1268). IGI Global. https://doi.org/10.4018/978-1-7998-5339-8.ch060

Chicago

Kumar, Harendra, Nutan Kumari Chauhan, and Pradeep Kumar Yadav. "A High Performance Model for Task Allocation in Distributed Computing System Using K-Means Clustering Technique." In Research Anthology on Architectures, Frameworks, and Integration Strategies for Distributed and Cloud Computing, edited by Information Resources Management Association, 1244-1268. Hershey, PA: IGI Global, 2021. https://doi.org/10.4018/978-1-7998-5339-8.ch060

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Abstract

Tasks allocation is an important step for obtaining high performance in distributed computing system (DCS). This article attempts to develop a mathematical model for allocating the tasks to the processors in order to achieve optimal cost and optimal reliability of the system. The proposed model has been divided into two stages. Stage-I, makes the ‘n' clusters of set of ‘m' tasks by using k-means clustering technique. To use the k-means clustering techniques, the inter-task communication costs have been modified in such a way that highly communicated tasks are clustered together to minimize the communication costs between tasks. Stage-II, allocates the ‘n' clusters of tasks onto ‘n' processors to minimize the system cost. To design the mathematical model, executions costs and inter tasks communication costs have been taken in the form of matrices. To test the performance of the proposed model, many examples are considered from different research papers and results of examples have compared with some existing models.

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