| Summary: | Cloud computing is a promising platform to conduct large-scale workflow applications according to the pay-per-use model. Minimizing the energy consumption of precedence constrained workflows with cost budget constraints has become one of the popular topics in cloud data centers. Most existing scheduling algorithms mainly consider execution time or cost of a given workflow application under a budget constraint; however, these algorithms do not adequately consider energy saving. A reducing energy consumption strategy using a critical task remapping (RMREC) algorithm is proposed in this study. This algorithm is decomposed into two phases: energy consumption reduction and critical task remapping. In the first phase, the adjustable cost budget and spare cost are determined on the basis of cost budget, critical task path, and adjustable budget factor. All workflow tasks are further allocated to virtual machines (VMs) with the lowest energy consumption to achieve preliminary mapping between tasks and VMs while satisfying the adjustable cost budget constraint. In the second phase, critical tasks are remapped to VMs according to spare cost to decrease energy consumption caused by task migration. Experiments on two types of workflow applications with different scales demonstrate that the presented RMREC algorithm effectively reduces energy consumption without violating cost budget constraints compared with existing algorithms.
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