CFP last date
20 May 2024
Call for Paper
June Edition
IJCA solicits high quality original research papers for the upcoming June edition of the journal. The last date of research paper submission is 20 May 2024

Submit your paper
Know more
Reseach Article

A Map Reduce Implementation on Open Source Platform: EUCALYPTUS

Published on September 2012 by Nilesh Mangtani, Jyoti B. Rathi
National Conference "MEDHA 2012"
Foundation of Computer Science USA
MEDHA - Number 1
September 2012
Authors: Nilesh Mangtani, Jyoti B. Rathi
0bc00035-b3bf-46dc-a063-88c275e2ec27

Nilesh Mangtani, Jyoti B. Rathi . A Map Reduce Implementation on Open Source Platform: EUCALYPTUS. National Conference "MEDHA 2012". MEDHA, 1 (September 2012), 30-34.

@article{
author = { Nilesh Mangtani, Jyoti B. Rathi },
title = { A Map Reduce Implementation on Open Source Platform: EUCALYPTUS },
journal = { National Conference "MEDHA 2012" },
issue_date = { September 2012 },
volume = { MEDHA },
number = { 1 },
month = { September },
year = { 2012 },
issn = 0975-8887,
pages = { 30-34 },
numpages = 5,
url = { /proceedings/medha/number1/8675-1016/ },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Proceeding Article
%1 National Conference "MEDHA 2012"
%A Nilesh Mangtani
%A Jyoti B. Rathi
%T A Map Reduce Implementation on Open Source Platform: EUCALYPTUS
%J National Conference "MEDHA 2012"
%@ 0975-8887
%V MEDHA
%N 1
%P 30-34
%D 2012
%I International Journal of Computer Applications
Abstract

Cloud computing is one of the important emerging technologies now-a-days. In recent years many of the applications are developed by using the Cloud computing. It mainly works by using the clusters of all the available resources in an organization or a company. Also recently Hadoop framework has also emerged which work in the distributed environments only. Hadoop being a open-source is used by many companies recently. In this paper, we have tried to propose a solution of merging the Hadoop technology with the cloud by using a open-source platform EUCALYPTUS. Since both of the above platforms are open source many of the companies can earn more profit by integrating with them. In this case the MapReduce an important part of Hadoop is being discussed and is tried to merge out with the Cloud by using EUCALYPTUS. MapReduce is a programming model that is developed by Google but widely used by Hadoop. Thus in this paper we have discussed few of scenarios where Hadoop can fails and also proposed the solution of those by using the Cloud technology.

References
  1. Microsoft. Microsoft private cloud. http://www. microsoft. com/virtualization/en/us/private-cloud. aspx, 2011. Retrieved 2011-04-26.
  2. VMware. Vmwarevcloud. http://www. microsoft. com/virtualization/en/us/private-cloud. aspx, 2011. Retrieved 2011-04-26.
  3. Citrix. Citrix open cloud platform. http://www. citrix. com/English/ps2/products/subfeature. asp?contentID=2303748, 2011. Retrieved 2011-04-26.
  4. OpenNebula. Opennebula - the opensource toolkit for cloud computing. http://opennebula. org/, 2011. Retrieved 2011-04-26.
  5. Cloud. com. The cloud os for the modern datacenter. http://cloud. com/, 2011. Retrieved 2011-04-26.
  6. G. Orenstein, "Digging Deeper Into Data With Hadoop," Available at http://gigaom. com/2009/06/07/digging-deeper-into-data-with-hadoop, 2009
  7. Jeffrey Dean and Sanjay Ghemawat, "MapReduce: Simplified Data Processing on Large Clusters", Symposium on Operating Systems Design and Implementation, 2004
  8. Hadoop: The Definitive Guide, Tom Wbite, 2010
  9. Hadoop in Action, Chuck Lam, 2010
  10. NCHC Cloud Computing Research Group website, http://trac. nchc. org. tw/cloud
  11. Daniel Peng and Frank Dabek, "Large-scale Incremental Processing Using Distributed Transactions and Notifications", Operating Systems Design and Implementation, Oct. 2010
  12. T. White. Hadoop - The Defenitive Guide. O'Reilly Media, 2nd edition, 2010.
  13. Dean J, Ghemawat S. MapReduce: Simplifed Data Processing on Large Clusters[C]//Proc. of the 6th Symposium on Operating System Design and Implementation, San Francisco. 2004.
  14. J Dean and S Ghemawat. Mapreduce: Simplied data processing on large clusters. In OSDI'04: Sixth Symposium on Operating System Design and Implementation. Google Inc. , 2004.
  15. ZHENG Xin-jie, ZHU Cheng-rong, XIONG Qi-bang, "Design and Implementation of Distributed Ray Tracing" . Computer Engineering. November 2007
  16. Eucaluptys Systems, Inc. Eucalyptus Administration Guide (2. 0), 2010.
Index Terms

Computer Science
Information Sciences

Keywords

Hadoop Mapreduce Cloud Computing Eucalyptus