Title :
Performance enhancement of Hadoop MapReduce framework for analyzing BigData
Author :
Prabhu, Swathi ; Rodrigues, Anisha P. ; Guru Prasad, M.S. ; Nagesh, H.R.
Author_Institution :
Dept. of CSE, NMAMIT, Nitte, India
Abstract :
In this BigData era processing and analyzing the data is very important and tedious job. An open source framework called Hadoop, implementation of MapReduce provides efficient platform for BigData analytics. The performance of Hadoop MapReduce mainly depends on its configuration parameters. Tuning the job configuration parameters is an effective way to improve performance so that we can reduce the execution time and the disk utilization. The performance tuning mainly based on CPU usage, disk I/O rate, memory usage, network traffic components. In this paper we are discussing the tuning methods to enhance the performance of MapReduce jobs. From our experiment we can say that performance has improved by 32.97% over the baseline system.
Keywords :
Big Data; data analysis; input-output programs; parallel processing; public domain software; Big Data; CPU usage; Hadoop; MapReduce; data analysis; disk I/O rate; memory usage; network traffic components; open source framework; performance enhancement; Buffer storage; Random access memory; Baseline system; BigData; Hadoop; MapReduce; Performance;
Conference_Titel :
Electrical, Computer and Communication Technologies (ICECCT), 2015 IEEE International Conference on
Conference_Location :
Coimbatore
Print_ISBN :
978-1-4799-6084-2
DOI :
10.1109/ICECCT.2015.7226049
