Unsupervised power modeling of co-allocated workloads for energy efficiency in data centers

Data centers are huge power consumers and their energy consumption keeps on rising despite the efforts to increase energy efficiency. A great body of research is devoted to the reduction of the computational power of these facilities, applying techniques such as power budgeting and power capping in servers. Such techniques rely on models to predict the power consumption of servers. However, estimating overall server power for arbitrary applications when running co-allocated in multithreaded servers is not a trivial task. In this paper, we use Grammatical Evolution techniques to predict the dynamic power of the CPU and memory subsystems of an enterprise server using the hardware counters of each application. On top of our dynamic power models, we use fan and temperature-dependent leakage power models to obtain the overall server power. To train and test our models we use real traces from a presently shipping enterprise server under a wide set of sequential and parallel workloads running at various frequencies We prove that our model is able to predict the power consumption of two different tasks co-allocated in the same server, keeping error below 8W. For the first time in literature, we develop a methodology able to combine the hardware counters of two individual applications, and estimate overall server power consumption without running the co-allocated application. Our results show a prediction error below 12W, which represents a 7.3% of the overall server power, outperforming previous approaches in the state of the art.