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Parallel Data Processing in the Cloud using Nephele

by Mayura D. Tapkire, B. M. Patil, V. M. Chandode
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 69 - Number 17
Year of Publication: 2013
Authors: Mayura D. Tapkire, B. M. Patil, V. M. Chandode
10.5120/12060-7527

Mayura D. Tapkire, B. M. Patil, V. M. Chandode . Parallel Data Processing in the Cloud using Nephele. International Journal of Computer Applications. 69, 17 ( May 2013), 1-8. DOI=10.5120/12060-7527

@article{ 10.5120/12060-7527,
author = { Mayura D. Tapkire, B. M. Patil, V. M. Chandode },
title = { Parallel Data Processing in the Cloud using Nephele },
journal = { International Journal of Computer Applications },
issue_date = { May 2013 },
volume = { 69 },
number = { 17 },
month = { May },
year = { 2013 },
issn = { 0975-8887 },
pages = { 1-8 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume69/number17/12060-7527/ },
doi = { 10.5120/12060-7527 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T21:30:29.558103+05:30
%A Mayura D. Tapkire
%A B. M. Patil
%A V. M. Chandode
%T Parallel Data Processing in the Cloud using Nephele
%J International Journal of Computer Applications
%@ 0975-8887
%V 69
%N 17
%P 1-8
%D 2013
%I Foundation of Computer Science (FCS), NY, USA
Abstract

In recent years, Infrastructure-as-a-Service (IaaS) clouds have become increasingly popular as a flexible and inexpensive platform for ad-hoc parallel data processing. Major players in cloud computing have started to integrate frameworks for parallel data processing in their product portfolio, making it easy for customers to access these services and to deploy their programs. However, currently used processing frameworks have been designed for static, homogeneous cluster systems and do not support the new features which distinguish the cloud platform. In this paper discussion is being done on the research project Nephele. Nephele is the first data processing framework to explicitly exploit the dynamic resource allocation offered by today's IaaS clouds for both, task scheduling and execution. First performance results of Nephele are presented and its efficiency is compared with one of the well-known software, MapReduce. MapReduce is chosen for comparison since it is open source software and currently enjoys high popularity in the data processing community.

References
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Index Terms

Computer Science
Information Sciences

Keywords

Cloud computing parallel data processing Nephele MapReduce

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