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Rio De Janeiro
May 14, 2007 to May 17, 2007
ISBN: 0-7695-2833-3
pp: 401-409
Arun Ramakrishnan , University of Southern California, Los Angeles, USA
Henan Zhao , University of Manchester, Manchester M13 9PL, UK
Rizos Sakellariou , University of Manchester, Manchester M13 9PL, UK
Kent Blackburn , California Institute of Technology
David Meyers , Northrop Grumman Information Technology, Pasadena
Michael Samidi , California Institute of Technology
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.
Arun Ramakrishnan, Gurmeet Singh, Henan Zhao, Ewa Deelman, Rizos Sakellariou, Karan Vahi, Kent Blackburn, David Meyers, Michael Samidi, "Scheduling Data-IntensiveWorkflows onto Storage-Constrained Distributed Resources", CCGRID, 2007, Seventh IEEE International Symposium on Cluster Computing and the Grid (CCGrid '07), Seventh IEEE International Symposium on Cluster Computing and the Grid (CCGrid '07) 2007, pp. 401-409, doi:10.1109/CCGRID.2007.101
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