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Published Articles >> Table of Contents >> Abstract
Seventh IEEE International Symposium on Cluster Computing and the Grid (CCGrid '07)
pp. 401-409
Scheduling Data-IntensiveWorkflows onto Storage-Constrained Distributed Resources
1 Ramakrishnan, University of Southern California, Los Angeles, USA
1 Singh, USC
1 Zhao, University of Manchester, Manchester M13 9PL, UK
1 Deelman, USC
1 Sakellariou, University of Manchester, Manchester M13 9PL, UK
1 Vahi, USC
1 Blackburn, California Institute of Technology
1 Meyers, Northrop Grumman Information Technology, Pasadena
1 Samidi, California Institute of Technology
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DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/CCGRID.2007.101
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| Abstract |
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In this paper we examine the issue of optimizing disk usage and of scheduling large-scale scientific workflows onto distributed resources where the workflows are dataintensive, requiring large amounts of data storage, and where the resources have limited storage resources. Our approach is two-fold: we minimize the amount of space a workflow requires during execution by removing data files at runtime when they are no longer required and we schedule the workflows in a way that assures that the amount of data required and generated by the workflow fits onto the individual resources. For a workflow used by gravitationalwave physicists, we were able to improve the amount of storage required by the workflow by up to 57 %. We also designed an algorithm that can not only find feasible solutions for workflow task assignment to resources in diskspace constrained environments, but can also improve the overall workflow performance.
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 Additional Information
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Citation:
1 Ramakrishnan, 1 Singh, 1 Zhao, 1 Deelman, 1 Sakellariou, 1 Vahi, 1 Blackburn, 1 Meyers, 1 Samidi,
"Scheduling Data-IntensiveWorkflows onto Storage-Constrained Distributed Resources,"
ccgrid,
pp. 401-409,
Seventh IEEE International Symposium on Cluster Computing and the Grid (CCGrid '07),
2007
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