The ever-increasing demand for scalable database systems is limited by their energy consumption, which is one of the major challenges in research today. While existing approaches mainly focused on transaction-oriented disk-based database systems, we are investigating and optimizing the energy consumption and performance of data-oriented scale-up in-memory database systems that make heavy use of the main power consumers, which are processors and main memory. In this demo, we present energy-utility functions as an approach for enabling the operating system to improve the energy efficiency of scalable in-memory database systems. Our highly interactive demo setup mainly allows attendees to switch between multiple DBMS workloads and watch in detail how the system responds by adapting the hardware configuration appropriately.

The trends in virtualization as well as multi-core, multiprocessor environments have translated to a massive increase in the amount of main memory each individual system needs to be fitted with, so as to effectively utilize this growing compute capacity. The increasing demand on main memory implies that the main memory devices and their issues are as important a part of system design as the central processors. The primary issues of modern memory are power, energy, and scaling of capacity. Nearly a third of the system power and energy can be from the memory subsystem. At the same time, modern main memory devices are limited by technology in their future ability to scale and keep pace with the modern program demands thereby requiring exploration of alternatives to main memory storage technology. This dissertation exploits dynamic knowledge of memory state and memory data value to improve memory performance and reduce memory energy consumption. A cross-boundary approach to communicate information about dynamic memory management state (allocated and deallocated memory) between software and hardware viii memory subsystem through a combination of ISA support and hardware structures is proposed in this research. These mechanisms help identify memory operations to regions of memory that have no impact on the correct execution of the program because they were either freshly allocated or deallocated. This inference about the impact stems from the fact that, data in memory regions that have been deallocated are no longer useful to the actual program code and data present in freshly allocated memory is also not useful to the program because the dynamic memory has not been defined by the program. By being cognizant of this, such memory operations are avoided thereby saving energy and improving the usefulness of the main memory. Furthermore, when stores write zeros to memory, the number of stores to the memory is reduced in this research by capturing it as compressed information which is stored along with memory management state information. Using the methods outlined above, this dissertation harnesses memory management state and data value information to achieve significant savings in energy consumption while extending the endurance limit of memory technologies. / text