Abstract
This article addresses the scheduling of heterogeneous scientific workflows while minimizing the energy consumption of the cloud provider, by introducing a deadline sensitive algorithm. Scheduling in a cloud environment is a difficult optimization problem. Usually, work around the scheduling of scientific workflows focuses on public clouds where infrastructure management is an unknown black box. Thus, many works offer scheduling algorithms designed to select the best set of virtual machines over time, so that the cost to the end user is minimized. This article presents a new HEFT-based algorithm that takes into account users deadlines to minimize the number of machines used by the cloud provider. The results show the real benefits of using our algorithm for reducing the energy consumption of the cloud provider.
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