Comprehend the Performance of MapReduce Programming model for K-Means algorithm on Hadoop Cluster
Chitresh Verma1, Rajiv Pandey2, Devesh Katiyar3

1Chitresh Verma, PhD scholar, Amity Institute of Information Technology, Amity University, (Uttar Pradesh) India.
2Dr. Rajiv Pandey, Senior Member IEEE. Amity Institute of Information Technology, Amity University, (Uttar Pradesh) India.
3Dr. Devesh Katiyar, Department of Computer Science at Dr. Shakuntala Misra National Rehabilitation University, Lucknow, (Uttar Pradesh) India.

Manuscript received on 30 June 2019 | Revised Manuscript received on 05 July 2019 | Manuscript published on 30 July 2019 | PP: 1600-1603 | Volume-8 Issue-9, July 2019 | Retrieval Number: I8129078919/19©BEIESP | DOI: 10.35940/ijitee.I8129.078919
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Abstract: MapReduce is a programming model used for processing Big Data. There are had been considerable research in improvement of performance of MapReduce model. This paper examines performance of MapReduce model based on K Means algorithm inside the Hadoop cluster. Different input size had been taken on various configurations to discover the impact of CPU cores and primary memory size. Results of this evaluation had been shown that the number of cores had maximum impact of the performance of MapReduce model.
Keywords: MapReduce, Hadoop, K-means algorithm, Data mining, cluster algorithm, Big Data, Performance evaluation.

Scope of the Article: Logic, Functional programming and Microcontrollers for IoT