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This paper examines issues related to the execution of scientific applications, and in particular computational workflows, on Cloud-based infrastructure. The paper describes the layering of application-level schedulers on top of the Cloud resources that enables grid-based applications to run on the Cloud. Finally, the paper examines issues of Cloud data management that supports workflow execution. We show how various ways of handling data have impact on the cost of the overall computations.
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