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

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.

Task Allocation Model for Optimal System Cost Using Fuzzy C-Means Clustering Technique in Distributed System

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“…The execution time, eik, of each task on each processor can be written in the form of Execution Time Matrix (ETM). The Total Execution Time (ET) is calculated as given [16]:…”

“…Total Inter-Task Communication Time (ITCT) of program is calculated by using Eq. 2given as follows [16]:…”

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