Dynamic Energy Estimation of Query Plans in Database Systems

Data centers are well known to consume large amounts of energy. Since database is one of the major applications in a typical data center, building energy-aware database systems has become an active research topic recently. The quantification of the energy cost of database systems is an important task in designing such systems. In this paper, we report our recent efforts on this topic, with a focus on the energy cost estimation of query plans during query optimization. We start from building a series of physical models for energy estimation of individual relational operators based on their resource consumption patterns. Since the execution of individual queries is a combination of relational operators, we use the physical models as a basis for a comprehensive energy cost estimation model for entire query plans. To further improve model accuracy under system dynamics and the variations of workload characteristics, we develop an online model estimation scheme that dynamically corrects the static model based on advanced modeling techniques adopted from control engineering. The models are implemented in a real database and evaluated on a physical test bed with a comprehensive set of experimental workloads. The results show that our solution achieves a high accuracy (above 90%) in energy estimation despite noises from the system and workloads.