Maintaining a sustainable society via technological innovations has been a major challenge for computing system design. In this project, we focus on the energy efficiency of an important type of computer applications, the database management systems (DBMS), which often consume a large portion of the computing resources and energy in modern data centers. The goal of this project is to design and implement a DBMS that enables significant energy conservation with graceful degradation of query processing performance. The project achieves its goal using the following approaches: (1) Dynamically exploit the energy-performance tradeoffs in DBMS as well as low-power modes of hardware systems for improved energy efficiency with performance guarantees; (2) Formulate the energy-efficient DBMS design as a feedback control problem, and adopt appropriate formal control techniques to achieve the desired performance and energy efficiency with theoretical analysis and guarantees; (3) Apply advanced multi-stage optimization methods to solve complex energy-aware storage management problems; and (4) Coordinate various control and optimization loops in different layers of the DBMS for maximized energy savings and global system stability. The broader impacts of the project are at two levels. At the society level, the reduction of energy consumption and CO2 emission by implementing the proposed energy-aware DBMS can be substantial. The project can benefit a large number of industrial sectors, the operations of which depend heavily on database-supported software such as online retailing systems, financial management software, and social networking platforms, by significantly lowering their electricity cost. At the education level, the project trains Ph.D. students in an interdisciplinary environment and enhances several courses taught at the University of Florida and the University of Tennessee at Knoxville by providing a rich set of application examples, software tools, and project opportunities. Publications, technical reports, software and experimental data from this project can be found at www.cse.usf.edu/~ytu/EDBMS.