Workload-Aware CPU Performance Scaling for Transactional Database Systems

Natural short term fluctuations in the load of transactional data systems present an opportunity for power savings. For example, a system handling 1000 requests per second on average can expect more than 1000 requests in some seconds, fewer in others. By quickly adjusting processing capacity to match such fluctuations, power consumption can be reduced. Many systems do this already, using dynamic voltage and frequency scaling (DVFS) to reduce processor performance and power consumption when the load is low. DVFS is typically controlled by frequency governors in the operating system, or by the processor itself. In this paper, we show that transactional database systems can manage DVFS more effectively than the underlying operating system. This is because the database system has more information about the workload, and more control over that workload, than is available to the operating system. We present a technique called POLARIS for reducing the power consumption of transactional database systems. POLARIS directly manages processor DVFS and controls database transaction scheduling. Its goal is to minimize power consumption while ensuring the transactions are completed within a specified latency target. POLARIS is workload-aware, and can accommodate concurrent workloads with different characteristics and latency budgets. We show that POLARIS can simultaneously reduce power consumption and reduce missed latency targets, relative to operating-system-based DVFS governors.