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Issue No.04 - July-Aug. (2013 vol.15)
pp: 20-29
Gideon Juve , University of Southern California
Mats Rynge , University of Southern California
Ewa Deelman , University of Southern California
Jens-S. Vockler , University of Southern California
G. Bruce Berriman , California Institute of Technology
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.
INDEX TERMS
Cloud computing, Computer architecture, Data transfer, Extrasolar planets, Grid computing, Astronomy, Scientific computing, Online services,scientific computing, grid computing, cloud computing, scientific workflows, Amazon EC2, open science grid, FutureGrid, astronomy
CITATION
Gideon Juve, Mats Rynge, Ewa Deelman, Jens-S. Vockler, G. Bruce Berriman, "Comparing FutureGrid, Amazon EC2, and Open Science Grid for Scientific Workflows", Computing in Science & Engineering, vol.15, no. 4, pp. 20-29, July-Aug. 2013, doi:10.1109/MCSE.2013.44
REFERENCES
1. Committee for a Decadal Survey of Astronomy and Astrophysics, Nat'l Research Council, New Worlds, New Horizons in Astronomy and Astrophysics, Nat'l Academies Press, 2010.
2. E. Deelman et al., “Pegasus: A Framework for Mapping Complex Scientific Workflows onto Distributed Systems,” Scientific Programming, vol. 13, no. 3, 2005, pp. 219-237.
3. I. Foster, C. Kesselman, and S. Tuecke, “The Anatomy of the Grid: Enabling Scalable Virtual Organizations,” Int'l J. High-Performance Computing Applications, vol. 15, no. 3, 2001, pp. 200-222.
4. D. Nurmi et al., “The Eucalyptus Open-Source Cloud-Computing System,” Proc. 9th IEEE/ACM Int'l Symp. Cluster Computing and the Grid (CCGrid 09), IEEE CS, 2009, pp. 124-131.
5. G. Juve and E. Deelman, “Automating Application Deployment in Infrastructure Clouds,” Proc. 3rd IEEE Int'l Conf. Cloud Computing Technology and Science (CloudCom), IEEE, 2011, pp. 658-665; doi:10.1109/CloudCom.2011.102.
6. I. Sfiligoi, “GlideinWMS—A Generic Pilot-Based Workload Management System,” J. Physics: Conf. Series, vol. 119, no. 6, 2008, p. 062044.
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