Power-Aware Consolidation of Scientific Workflows in Virtualized Environments

The recent emergence of clouds with large, virtualized pools of compute and storage resources raises the possibility of a new compute paradigm for scientific research. With virtualization technologies, consolidation of scientific workflows presents a promising opportunity for energy and resource cost optimization, while achieving high performance. We have developed pSciMapper, a power-aware consolidation framework for scientific workflows. We view consolidation as a hierarchical clustering problem, and introduce a distance metric that is based on interference between resource requirements. A dimensionality reduction method (KCCA) is used to relate the resource requirements to performance and power consumption. We have evaluated pSciMapper with both real-world and synthetic scientific workflows, and demonstrated that it is able to reduce power consumption by up to 56%, with less than 15% slowdown. Our experiments also show that scheduling overheads of pSciMapper are low, and the algorithm can scale well for workflows with hundreds of tasks.