Energy prediction for I/O intensive workflow applications

As workflow-based data-intensive applications have become increasingly popular, the lack of support tools to aid resource provisioning decisions, to estimate the energy cost of running such applications, or simply to support configuration choices has become increasingly evident. The goal of this thesis is to design techniques and tools to predict the energy consumption of these workflow-based applications, evaluate different optimization techniques from an energy perspective, and explore energy/performance tradeoffs. This thesis proposes a methodology to predict the energy consumption for workflow applications. More concretely, it makes three key contributions: First, it proposes a simple analytical energy consumption model that enables adequately accurate energy consumption predictions. This makes it possible not only to estimate energy consumption but also to reason about the relative benefits different system configuration and provisioning decisions offer. Second, an empirical evaluation of energy consumption is carried out using synthetic benchmarks and real workflow applications. This evaluation quantifies the energy savings of performance optimizations for the distributed storage system as well as the energy and performance impact of power-centric tuning techniques. Third, it demonstrates the predictor’s ability to expose energy performance tradeoffs for the synthetic benchmarks and workflow applications by evaluating the accuracy of the energy consumption predictions. Overall, the prediction obtained an average accuracy of more than 85% and a median of 90% across different scenarios, while using less than 200x less resources than running than actual applications.Applied Science, Faculty ofElectrical and Computer Engineering, Department ofGraduat