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Parallel Data Processing for Effective Dynamic Resource Allocation in the Cloud

by K. Krishna Jyothi
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 70 - Number 22
Year of Publication: 2013
Authors: K. Krishna Jyothi
10.5120/12196-7913

K. Krishna Jyothi . Parallel Data Processing for Effective Dynamic Resource Allocation in the Cloud. International Journal of Computer Applications. 70, 22 ( May 2013), 1-4. DOI=10.5120/12196-7913

@article{ 10.5120/12196-7913,
author = { K. Krishna Jyothi },
title = { Parallel Data Processing for Effective Dynamic Resource Allocation in the Cloud },
journal = { International Journal of Computer Applications },
issue_date = { May 2013 },
volume = { 70 },
number = { 22 },
month = { May },
year = { 2013 },
issn = { 0975-8887 },
pages = { 1-4 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume70/number22/12196-7913/ },
doi = { 10.5120/12196-7913 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T21:33:30.563815+05:30
%A K. Krishna Jyothi
%T Parallel Data Processing for Effective Dynamic Resource Allocation in the Cloud
%J International Journal of Computer Applications
%@ 0975-8887
%V 70
%N 22
%P 1-4
%D 2013
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Parallel data processing has become more and more reliable phenomenon due to the realization of could computing, especially using IaaS (Infrastructure as a Service) clouds. The cloud service providers such as IBM, Google, Microsoft and Oracle have made provisions for parallel data processing in their cloud services. Nevertheless, the frameworks used as of now are static and homogenous in nature in a cluster environment. The problem with these frameworks is that the resource allocation when large jobs are submitted is not efficient as they take more time for processing besides incurring more cost. In this paper we discuss the possibilities of parallel processing and its challenges. One of the IaaS products meant for parallel processing is presented in this paper. VMs are allocated to tasks dynamically for execution of jobs. With proposed framework we performed parallel job processing which involves Map Reduce, a new programming phenomenon. We also compare this with Hadoop.

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Index Terms

Computer Science
Information Sciences

Keywords

Parallel processing cloud computing Map Reduce many-task computing

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