WattTime: Novel System Power Model and Completion Time Model for DVFS-Enabled Servers

Power consumption costs takes up to half of the operational expenses of data centers, making power management a critical concern. Advances in processor technology provide fine-grained control over operating frequency of processors and this control can be used to trade off power for performance. We show that existing power models incorrectly assume quadratic relationship between power and frequency, leading to higher inaccuracy in prediction. Moreover, existing performance models have significant error margins while predicting performance of memory or file-intensive tasks and HPC applications due to negligence of the combined effects of frequency and CPU variations on the task execution time. In this paper, we empirically derive power and completion time models using linear regression with CPU utilization and operating frequency as parameters. We validate our power model on several Intel and AMD processors by predicting within 2-7% of measured power. We validate our completion time model using five kernels of NASA Parallel Benchmark suite and five CPU, memory and file-intensive benchmarks on four heterogeneous systems and predicting within 1-6% of observed performance.