research-article

Comparing FutureGrid, Amazon EC2, and Open Science Grid for Scientific Workflows

Authors:

Graham BerrimanAuthors Info & Claims

Computing in Science and Engineering, Volume 15, Issue 4

Pages 20 - 29

https://doi.org/10.1109/MCSE.2013.44

Published: 01 July 2013 Publication History

Abstract

Scientists have many computing infrastructures available to conduct their research, including grids and public or private clouds. This article explores the use of these cyberinfrastructures to execute scientific workflows, an important class of scientific applications. In particular, it examines the benefits and drawbacks of cloud and grid systems using the case study of an astronomy application that analyzes data from the NASA Kepler mission to compute periodograms. The authors describe their experiences modeling the periodogram application as a scientific workflow using Pegasus and deploying it on the FutureGrid scientific cloud testbed, the Amazon EC2 commercial cloud, and the Open Science Grid. They also compare and contrast the infrastructures in terms of setup, usability, cost, resource availability, and performance.

Cited By

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Poola DRamamohanarao KBuyya R(2016)Enhancing Reliability of Workflow Execution Using Task Replication and Spot InstancesACM Transactions on Autonomous and Adaptive Systems10.1145/281562410:4(1-21)Online publication date: 3-Feb-2016
https://dl.acm.org/doi/10.1145/2815624
Zhu ZZhang GLi MLiu X(2016)Evolutionary Multi-Objective Workflow Scheduling in CloudIEEE Transactions on Parallel and Distributed Systems10.1109/TPDS.2015.244645927:5(1344-1357)Online publication date: 1-May-2016
https://dl.acm.org/doi/10.1109/TPDS.2015.2446459
Deelman EVahi KRynge MJuve GMayani Rda Silva R(2016)Pegasus in the Cloud: Science Automation through Workflow TechnologiesIEEE Internet Computing10.1109/MIC.2016.1520:1(70-76)Online publication date: 1-Jan-2016
https://dl.acm.org/doi/10.1109/MIC.2016.15
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Comparing FutureGrid, Amazon EC2, and Open Science Grid for Scientific Workflows

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cover image Computing in Science and Engineering

Computing in Science and Engineering Volume 15, Issue 4

July 2013

80 pages

ISSN:1521-9615

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Publisher

IEEE Educational Activities Department

United States

Publication History

Published: 01 July 2013

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Poola DRamamohanarao KBuyya R(2016)Enhancing Reliability of Workflow Execution Using Task Replication and Spot InstancesACM Transactions on Autonomous and Adaptive Systems10.1145/281562410:4(1-21)Online publication date: 3-Feb-2016
https://dl.acm.org/doi/10.1145/2815624
Zhu ZZhang GLi MLiu X(2016)Evolutionary Multi-Objective Workflow Scheduling in CloudIEEE Transactions on Parallel and Distributed Systems10.1109/TPDS.2015.244645927:5(1344-1357)Online publication date: 1-May-2016
https://dl.acm.org/doi/10.1109/TPDS.2015.2446459
Deelman EVahi KRynge MJuve GMayani Rda Silva R(2016)Pegasus in the Cloud: Science Automation through Workflow TechnologiesIEEE Internet Computing10.1109/MIC.2016.1520:1(70-76)Online publication date: 1-Jan-2016
https://dl.acm.org/doi/10.1109/MIC.2016.15
(2016)Cost optimization approaches for scientific workflow scheduling in cloud and grid computingJournal of Systems and Software10.1016/j.jss.2015.11.023113:C(1-26)Online publication date: 1-Mar-2016
https://dl.acm.org/doi/10.1016/j.jss.2015.11.023
Saad WAbbes HCérin CJemni M(2016)A self-organized volunteer Cloud for e-ScienceThe Journal of Supercomputing10.1007/s11227-015-1564-z72:4(1271-1290)Online publication date: 1-Apr-2016
https://dl.acm.org/doi/10.1007/s11227-015-1564-z
Alkhanak ELee SKhan S(2015)Cost-aware challenges for workflow scheduling approaches in cloud computing environmentsFuture Generation Computer Systems10.1016/j.future.2015.01.00750:C(3-21)Online publication date: 1-Sep-2015
https://dl.acm.org/doi/10.1016/j.future.2015.01.007
Zhao YLi YRaicu ILu STian WLiu H(2015)Enabling scalable scientific workflow management in the CloudFuture Generation Computer Systems10.1016/j.future.2014.10.02346:C(3-16)Online publication date: 1-May-2015
https://dl.acm.org/doi/10.1016/j.future.2014.10.023
Botón-Fernández MRodríguez-Pascual MVega-Rodríguez MPrieto-Castrillo FMayo-García R(2015)A Comparative Analysis of Adaptive Solutions for Grid EnvironmentsInternational Journal of Parallel Programming10.1007/s10766-014-0342-543:5(786-811)Online publication date: 1-Oct-2015
https://dl.acm.org/doi/10.1007/s10766-014-0342-5
Kim HChun KKim HChung Y(2015)Utilization of workflow management system for virtual machine instance management on cloudConcurrency and Computation: Practice & Experience10.1002/cpe.357927:17(5350-5373)Online publication date: 10-Dec-2015
https://dl.acm.org/doi/10.1002/cpe.3579
Leite ARaiol TTadonki CWalter MEisenbeis Cde Melo AJerzak ZRivière EVeiga L(2014)ExcaliburProceedings of the Fourth International Workshop on Cloud Data and Platforms10.1145/2592784.2592786(1-6)Online publication date: 13-Apr-2014
https://dl.acm.org/doi/10.1145/2592784.2592786

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Recommendations

Enhancing the Grid with Cloud Computing

Cloud Paradigms and Practices for Computational and Data-Enabled Science and Engineering

e-Clouds: Scientific Computing as a Service

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