Critical insight for MAPReduce optimization in Hadoop

In present day scenario cloud has become an inevitable need for majority of IT operational organization s. Cloud applications such as data storage, data retrieval and data portability have become significant requirements for cloud computing. Numerous applications are being developed for BigData. Ach...

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Main Authors:	Khan, Burhan Ul Islam, Olanrewaju, Rashidah Funke, Altaf, Hunain, Shah, Asadullah
Format:	Article
Language:	English
Published:	Open Science 2014
Subjects:	T10.5 Communication of technical information
Online Access:	http://irep.iium.edu.my/36441/ http://irep.iium.edu.my/36441/ http://irep.iium.edu.my/36441/2/download.pdf

id	iium-36441
recordtype	eprints
spelling	iium-364412018-06-19T08:21:02Z http://irep.iium.edu.my/36441/ Critical insight for MAPReduce optimization in Hadoop Khan, Burhan Ul Islam Olanrewaju, Rashidah Funke Altaf, Hunain Shah, Asadullah T10.5 Communication of technical information In present day scenario cloud has become an inevitable need for majority of IT operational organization s. Cloud applications such as data storage, data retrieval and data portability have become significant requirements for cloud computing. Numerous applications are being developed for BigData. Achieving an optimal approach for higher performance in terms of efficient load balancing, load distribution, optimum resource utilization, minimum overheads and least possible delay has been the vital issue for cloud infrastructure. Apache Hadoop is one the most used cloud frame work for cloud infrastructure. The predominant philosophy behind Hadoop optimization is the optimization of MapReduce, which is a dominant programming platform effective in bringing a=bout many functional enhancements as per scheduling algorithms developed and implemented. MapReduce has emerged as the most significant part of Hadoop system that establishes itself as a framework that can effectively simplify the overall complexity of running parallel data processes across the network of computing nodes. A number of scheduling techniques have been advocated in the last couple of years for achieving enhanced load balancing in Hadoop. Unfortunately Hadoop still lacks a system model that could facilitate an ultimate solution for delivering optimized performance without creating much computational overhead. In order to pave a way for the development of an adept and decisive load balancing and job scheduling scheme for minimum execution time and optimum resource utilization in future, here in this paper a comprehensive review of some of the major works has been done to discuss the prominence of issues, which will be needed to be taken care of while developing the same. Open Science 2014-04 Article PeerReviewed application/pdf en http://irep.iium.edu.my/36441/2/download.pdf Khan, Burhan Ul Islam and Olanrewaju, Rashidah Funke and Altaf, Hunain and Shah, Asadullah (2014) Critical insight for MAPReduce optimization in Hadoop. International Journal of Computer Science and Control Engineering, 2 (1). pp. 1-7. http://www.openscienceonline.com/journal/csce
repository_type	Digital Repository
institution_category	Local University
institution	International Islamic University Malaysia
building	IIUM Repository
collection	Online Access
language	English
topic	T10.5 Communication of technical information
spellingShingle	T10.5 Communication of technical information Khan, Burhan Ul Islam Olanrewaju, Rashidah Funke Altaf, Hunain Shah, Asadullah Critical insight for MAPReduce optimization in Hadoop
description	In present day scenario cloud has become an inevitable need for majority of IT operational organization s. Cloud applications such as data storage, data retrieval and data portability have become significant requirements for cloud computing. Numerous applications are being developed for BigData. Achieving an optimal approach for higher performance in terms of efficient load balancing, load distribution, optimum resource utilization, minimum overheads and least possible delay has been the vital issue for cloud infrastructure. Apache Hadoop is one the most used cloud frame work for cloud infrastructure. The predominant philosophy behind Hadoop optimization is the optimization of MapReduce, which is a dominant programming platform effective in bringing a=bout many functional enhancements as per scheduling algorithms developed and implemented. MapReduce has emerged as the most significant part of Hadoop system that establishes itself as a framework that can effectively simplify the overall complexity of running parallel data processes across the network of computing nodes. A number of scheduling techniques have been advocated in the last couple of years for achieving enhanced load balancing in Hadoop. Unfortunately Hadoop still lacks a system model that could facilitate an ultimate solution for delivering optimized performance without creating much computational overhead. In order to pave a way for the development of an adept and decisive load balancing and job scheduling scheme for minimum execution time and optimum resource utilization in future, here in this paper a comprehensive review of some of the major works has been done to discuss the prominence of issues, which will be needed to be taken care of while developing the same.
format	Article
author	Khan, Burhan Ul Islam Olanrewaju, Rashidah Funke Altaf, Hunain Shah, Asadullah
author_facet	Khan, Burhan Ul Islam Olanrewaju, Rashidah Funke Altaf, Hunain Shah, Asadullah
author_sort	Khan, Burhan Ul Islam
title	Critical insight for MAPReduce optimization in Hadoop
title_short	Critical insight for MAPReduce optimization in Hadoop
title_full	Critical insight for MAPReduce optimization in Hadoop
title_fullStr	Critical insight for MAPReduce optimization in Hadoop
title_full_unstemmed	Critical insight for MAPReduce optimization in Hadoop
title_sort	critical insight for mapreduce optimization in hadoop
publisher	Open Science
publishDate	2014
url	http://irep.iium.edu.my/36441/ http://irep.iium.edu.my/36441/ http://irep.iium.edu.my/36441/2/download.pdf
first_indexed	2023-09-18T20:52:12Z
last_indexed	2023-09-18T20:52:12Z
_version_	1777410069425553408

Critical insight for MAPReduce optimization in Hadoop

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