Empirical Web server power modeling and characterization

Commodity processors, which are prevalent in Internet-based data centers, do not have internal sensors for monitoring energy consumption. Such processors usually feature performance counters which can be used to indirectly estimate power consumption [1]. The usual approach in those studies is to derive linear power models based on the usage numbers collected for the processor sub-components such as caches and branch predictor. These models are usually targeted to CPU-bound applications which need more CPU performance counter parameters and display high CPU usage most of time. On a Web server environment, the applications are mostly I/O-bound which creates non-linear effects among server statistics of performance and power, making these models less suitable for Web servers. This paper presents a new approach for power models for Web servers, based on ranges of CPU usage values and performance server statistics. This new method softens non-linear relationship between server statistics and power consumption on linear power models improving their accuracy.