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Reseach Article

Enhance Performance of Mapreduce Job on Hadoop Framework using Setup and Cleanup

by Priyam Jain, Satyaranjan Patra, Pankaj Richhariya
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 155 - Number 8
Year of Publication: 2016
Authors: Priyam Jain, Satyaranjan Patra, Pankaj Richhariya
10.5120/ijca2016912400

Priyam Jain, Satyaranjan Patra, Pankaj Richhariya . Enhance Performance of Mapreduce Job on Hadoop Framework using Setup and Cleanup. International Journal of Computer Applications. 155, 8 ( Dec 2016), 36-40. DOI=10.5120/ijca2016912400

@article{ 10.5120/ijca2016912400,
author = { Priyam Jain, Satyaranjan Patra, Pankaj Richhariya },
title = { Enhance Performance of Mapreduce Job on Hadoop Framework using Setup and Cleanup },
journal = { International Journal of Computer Applications },
issue_date = { Dec 2016 },
volume = { 155 },
number = { 8 },
month = { Dec },
year = { 2016 },
issn = { 0975-8887 },
pages = { 36-40 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume155/number8/26628-2016912400/ },
doi = { 10.5120/ijca2016912400 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T00:00:45.625146+05:30
%A Priyam Jain
%A Satyaranjan Patra
%A Pankaj Richhariya
%T Enhance Performance of Mapreduce Job on Hadoop Framework using Setup and Cleanup
%J International Journal of Computer Applications
%@ 0975-8887
%V 155
%N 8
%P 36-40
%D 2016
%I Foundation of Computer Science (FCS), NY, USA
Abstract

MapReduce is an effective programming model for large-scale data-intensive computing applications. Hadoop is an open-source implementation of MapReduce which has been widely used. The communication overhead from the big data sets’ transmission affects the performance of Hadoop greatly. In consideration of data locality, Hadoop schedules tasks to the nodes near the data locations preferentially to decrease data transmission overhead, which works well in homogeneous and dedicated MapReduce environments. However, due to practical considerations about cost and resource utilization, it is common to maintain heterogeneous clusters or share resources by multiple users. Unfortunately, it’s difficult to take advantage of data locality in these heterogeneous or shared environments [1]. To improve the performance of MapReduce in heterogeneous or shared environments, a data prefetching mechanism is proposed, In this paper, we can fetch the data to corresponding compute nodes in advance. It is proved that the proposal of this paper reduces data transmission overhead effectively with theoretical analysis. We also work on applying similar prefetching mechanisms to other phases in MapReduce, and researching on predicting the execution nodes of tasks in cluster computing to improve performance and the result are clearly shows that proposed system will takes a less execution time as compared to existing mapreduce job.

References
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Index Terms

Computer Science
Information Sciences

Keywords

Big data Hadoop Mapreduce performance prefetching mechanism setup & cleanup