Online Energy Estimation of Relational Operations in Database Systems

Data centers are well known to consume a large amount of energy. As databases are one of the major applications in a 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 design. In this paper, we report our recent efforts on this issue, 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. As the execution of a query plan is a combination of multiple relational operators, we use the physical models as a basis for a comprehensive energy model for the entire query. To address the challenge of maintaining accuracy under system and workload dynamics, we develop an online scheme that dynamically adjusts model parameters based on statistical signal modeling. Our models are implemented in a real database management system and evaluated on a physical test bed. The results show that our solution achieves a high accuracy (worst-case error 13.7 percent) despite noises. Our models also help identify query plans with significantly higher energy efficiency.