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Large-scale applications expressed as scientific workflows are often grouped into ensembles of inter-related workflows. In this paper, the authors address a new and important problem concerning the efficient management of such ensembles under budget and deadline constraints on Infrastructure-as-a-Service (IaaS) clouds. They discuss, develop, and assess algorithms based on static and dynamic strategies for both task scheduling and resource provisioning. They perform the evaluation via simulation using a set of scientific workflow ensembles with a broad range of budget and deadline parameters, taking into account uncertainties in task runtime estimations, provisioning delays, and failures.
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