Dynamic Fine-Grained Scheduling for Energy-Efficient Main-Memory Queries

Power and cooling costs are some of the highest costs in data centers today, which make improvement in energy efficiency crucial. Energy efficiency is also a major design point for chips that power whole ranges of computing devices. One important goal in this area is energy proportionality, arguing that the system's power consumption should be proportional to its performance. Currently, a major trend among server processors, which stems from the design of chips for mobile devices, is the inclusion of advanced power management techniques, such as dynamic voltage-frequency scaling, clock gating, and turbo modes. A lot of recent work on energy efficiency of database management systems is focused on coarse-grained power management at the granularity of multiple machines and whole queries. These techniques, however, cannot efficiently adapt to the frequently fluctuating behavior of contemporary workloads. In this paper, we argue that databases should employ a fine-grained approach by dynamically scheduling tasks using precise hardware models. These models can be produced by calibrating operators under different combinations of scheduling policies, parallelism, and memory access strategies. The models can be employed at run-time for dynamic scheduling and power management in order to improve the overall energy efficiency. We experimentally show that energy efficiency can be improved by up to 4x for fundamental memory-intensive database operations, such as scans.