Optimizing Energy-Efficiency in High-Available Scientific Cloud Environments

Virtualization technologies empower construction of flexible computing environments, promising an opportunity for energy and resource cost optimization, while enhancing system availability and achieving high performance. A crucial requirement for effective consolidation is to be able to efficiently utilize system resources for high-availability computing, and energy-efficiency optimization, so as to reduce operational costs and carbon footprints to the environment. In this work, we propose a consolidation technique to improve the performance of energy- and reliability-aware scheduling algorithms. For that, we carefully tune an energy optimization mechanism, which detects energy optimizing opportunities, and executes power- and failure-aware decision making algorithms to readjust virtual-to-physical mappings. We conduct simulations injecting synthetic jobs which characteristics follow the last version of the Google Cloud trace logs. The results indicate that our strategy improves work per Joule ratio in about 9.7%, as well working-efficiency in almost 15.6%, maintaining similar levels of completion jobs.