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Reseach Article

A Survey on Data Placement Strategies for Cloud based Scientific Workflows

by Lalitha Singh, Jyoti Malhotra
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 141 - Number 6
Year of Publication: 2016
Authors: Lalitha Singh, Jyoti Malhotra

Lalitha Singh, Jyoti Malhotra . A Survey on Data Placement Strategies for Cloud based Scientific Workflows. International Journal of Computer Applications. 141, 6 ( May 2016), 30-33. DOI=10.5120/ijca2016909651

@article{ 10.5120/ijca2016909651,
author = { Lalitha Singh, Jyoti Malhotra },
title = { A Survey on Data Placement Strategies for Cloud based Scientific Workflows },
journal = { International Journal of Computer Applications },
issue_date = { May 2016 },
volume = { 141 },
number = { 6 },
month = { May },
year = { 2016 },
issn = { 0975-8887 },
pages = { 30-33 },
numpages = {9},
url = { },
doi = { 10.5120/ijca2016909651 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
%0 Journal Article
%1 2024-02-06T23:42:46.005784+05:30
%A Lalitha Singh
%A Jyoti Malhotra
%T A Survey on Data Placement Strategies for Cloud based Scientific Workflows
%J International Journal of Computer Applications
%@ 0975-8887
%V 141
%N 6
%P 30-33
%D 2016
%I Foundation of Computer Science (FCS), NY, USA

Scientific workflows perform computations exceeding single workstation’s capabilities. When running such data intensive workflows in the cloud distributed across several physical locations, the execution time and the resource utilization efficiency highly depends on the initial placement and distribution of the input datasets across these multiple virtual machines in the Cloud. The ideal data placement scheme optimizes the execution of the data intensive scientific workflows in cloud by assigning the tasks to the execution site in such a way that the file transfers and the cost associated are reduced. Several data placement strategies in cloud based scientific workflows are reviewed. A data placement scheme which uses big data to improve the performance and also the data movement cost is studied. BDAP (Big Data Placement strategy), improves workflow performance by minimizing data movement across multiple virtual machines.

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Index Terms

Computer Science
Information Sciences


Cloud computing Big data Scientific workflow Data placement Virtual machine.