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Performance Analysis of Hadoop with Pseudo-Distributed Mode on Different Machines

Ruchi Mittal1 , Ruhi Bagga2

Section:Research Paper, Product Type: Journal Paper
Volume-3 , Issue-6 , Page no. 113-117, Jun-2015

Online published on Jun 29, 2015

Copyright © Ruchi Mittal , Ruhi Bagga . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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IEEE Style Citation: Ruchi Mittal , Ruhi Bagga, “Performance Analysis of Hadoop with Pseudo-Distributed Mode on Different Machines,” International Journal of Computer Sciences and Engineering, Vol.3, Issue.6, pp.113-117, 2015.

MLA Style Citation: Ruchi Mittal , Ruhi Bagga "Performance Analysis of Hadoop with Pseudo-Distributed Mode on Different Machines." International Journal of Computer Sciences and Engineering 3.6 (2015): 113-117.

APA Style Citation: Ruchi Mittal , Ruhi Bagga, (2015). Performance Analysis of Hadoop with Pseudo-Distributed Mode on Different Machines. International Journal of Computer Sciences and Engineering, 3(6), 113-117.

BibTex Style Citation:
@article{Mittal_2015,
author = {Ruchi Mittal , Ruhi Bagga},
title = {Performance Analysis of Hadoop with Pseudo-Distributed Mode on Different Machines},
journal = {International Journal of Computer Sciences and Engineering},
issue_date = {6 2015},
volume = {3},
Issue = {6},
month = {6},
year = {2015},
issn = {2347-2693},
pages = {113-117},
url = {https://www.ijcseonline.org/full_paper_view.php?paper_id=561},
publisher = {IJCSE, Indore, INDIA},
}

RIS Style Citation:
TY - JOUR
UR - https://www.ijcseonline.org/full_paper_view.php?paper_id=561
TI - Performance Analysis of Hadoop with Pseudo-Distributed Mode on Different Machines
T2 - International Journal of Computer Sciences and Engineering
AU - Ruchi Mittal , Ruhi Bagga
PY - 2015
DA - 2015/06/29
PB - IJCSE, Indore, INDIA
SP - 113-117
IS - 6
VL - 3
SN - 2347-2693
ER -

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Abstract

Data cannot be managed by the traditional database management systems when it comes in a large amount. So there comes the Big Data. Hadoop and MapReduce are the solution to handle, manage and analyze Big Data. Hadoop is an open source implementation of MapReduce programming paradigm which is a parallel distributed programming model for handling large data intensive applications. In this paper, we present our experimental work done on Hadoop with pseudo-distributed mode on different machines and analyze the time taken by Hadoop to perform the same operations on different machines.

Key-Words / Index Term

Big Data; Hadoop; MapReduce; Pseudo-distributed Mode; Distributed Programming

References

[1] Xuelian Lin, Zide Meng, Chuan Xu, Meng Wang,”A Pratical Performance Model for Hadoop MapReduce”, in proc. Of the 2012 IEEE International Conference on Cluster Computing Workshops,ISBN: 978-1-4673-2893-7,Page No (231-239), Sept 24-28,2012.
[2] M. Maurya, S. Mahajan,”Performance Analysis of MapReduce Programs on Hadoop Cluster”, in proc. of 2012 World Congress on Information and Communication Technologies, ISBN:978-1-4673-4806-5,Page No (505-510), Oct 30-Nov 2,2012.
[3] M. Ishii, Jungkyu Han, H. Mankino,”Design and Performance Evaluation for Hadoop Clusters on Virtualized Environment”, in proc. of 2103 International Conference on Information Networking, E-ISBN:978-1-4673-5741-8, Page No (244-249), Jan 28-30,2013.
[4] Han Jungkyu, M. Ishii, H. Makino,”A Hadoop Performance Model For Multi-Rack Clusters”,in proc. of 2013 5th International Conference on Computer Science and Information Technology, Page No (265-274), Mar 27-27,2013.
[5] Zhuoyao Zhang, Ludmila Cherksova, Boon Thau Loo,”Performance Modeling od MapReduce Jobs in Heterogeneous Cloud Environments”, in proc. of the 2013 IEEE Sixth International Conference on Cloud Computing, ISBN: 978-0-7695-5028-2, Page No (839-846), June 28- July 3,2013.
[6] J. Nandimath, E. Banerjee, A.Patil, P. Kakade, “Big Data Analysis using Apache Hadoop”, in proc. of 2013 IEEE 14th International Conference on Information Reuse and Intergration, Page No (700-703), Aug 14-16,2013.
[7] A. Pal, K.Jain, P.Agarwal,S.Agarwal, “A Performance Analysis of MapReduce Task With Large Number of Files Dataset in Big Data Using Hadoop”, in proc. of 2014 Fourth International Conference on Communication Systems and Network Technologies, ISBN: 978-1-4799-3069-2, Page No (587-591), Apr 07-09,2014.
[8] Invanilton Polato, Reginaldo Re, Alfredo Goldman, Fabio Kon, “A Comprehensive view of Hadoop Research- A Systematic Literature Review”, Elsevier- Journal of Network and Computer Applications,Volume-46,Page No (1-25), Aug 2014.
[9] Chia-Wei Lee, Kuang-Yu Hsieh Sun-Yuan Hsieh , Hung-Chang Hsiao,”A Dynamic Data Placement Strategy for Hadoop in Heterogeneous Environments ”, Elsevier-Big Data Research, Volume-1, Page No (14-22), Aug 2014.
[10] D. Dev, R. Patgiri, “Performance Evaluation of HDFS in Big Data Management”, in proc. of 2014 International Conference on High Performance Computing and Applications,ISBN: 978-1-4799-5957-0, Page No (1-7), Dec 22-24,2014.
[11] M.F. Hyder, M.A. Ismail, H. Ahmed, “Performance Comparison of Hadoop Clusters Configured on Virtual Machines and as a Cloud Service”, in proc. of 2014 International Conference on International Technologies,ISBN: 978-1-4799-6088-0, Page No (60-64), Dec 8-9,2014.
[12] Hadoop Tutorial [online]. Available: https:// hadoop.apache.org