Enabling Efficient Power Provisioning for Enterprise Applications

The increasing demand for computation and the commensurate rise in the power density of data centers have led to increased costs associated with constructing and operating a data center. Exacerbating such costs, data centers are often over-provisioned to avoid costly outages associated with the potential overloading of electrical circuitry. However, such over-provisioning is often unnecessary since a data center rarely operates at its maximum capacity. It is imperative that we maximize the use of the available power budget in order to enhance the efficiency of data centers. On the other hand, introducing power constraints to improve the efficiency of a data center can cause unacceptable violation of performance agreements (i.e., throughput and response time constraints). As such, we present a thorough empirical study of performance under power constraints as well as a runtime system to set appropriate power constraints for meeting strict performance targets. In this paper, we design a runtime system based on a load prediction model and an optimization framework to set the appropriate power constraints to meet specific performance targets. We then present the effects of our runtime system on energy proportionality, average power, performance, and instantaneous power consumption of enterprise applications. Our results shed light on mechanisms to tune the power provisioned for a server under strict performance targets and opportunities to improve energy proportionality and instantaneous power consumption via power limiting.