Abstract: Methods, apparatus, and products as disclosed for estimating power consumption of computing components configured in a computing system that include: selecting, by a power estimation module, a plurality of calibration datasets from a calibration dataset repository, each calibration dataset specifying calibration power consumption by one or more computing components in the computing system for a calibration workload at a plurality of calibration operating points; measuring, by the power estimation module, a current power consumption by one or more measured computing components in the computing system for a current workload at a current operating point; determining, by the power estimation module, an estimated power consumption for the measured computing components at a proposed operating point in dependence upon the selected calibration datasets and the current power consumption for the current workload at the current operating point; and administering the computing system in dependence upon the estimated powe

Abstract: A performance measure (e.g., processor speed) for computing components such as servers is optimized by creating models of power consumption versus the performance measure for each server, adding the power models to derive an overall power model, and calculating an optimum set point for the performance measure which corresponds to a power limit on the servers using the overall power model. The set point is then used to set power budgets for the servers based on their power models, and the servers maintain power levels no greater than their respective power budgets. The server power models are preferably created in real time by monitoring power consumption and the performance measure to derive sets of data points for the servers, and performing regression on the sets of data points to yield power models for the servers. Multiple server power models may be created for different program applications.

Abstract: A performance measure (e.g., processor speed) for computing components such as servers is optimized by creating models of power consumption versus the performance measure for each server, adding the power models to derive an overall power model, and calculating an optimum set point for the performance measure which corresponds to a power limit on the servers using the overall power model. The set point is then used to set power budgets for the servers based on their power models, and the servers maintain power levels no greater than their respective power budgets. The server power models are preferably created in real time by monitoring power consumption and the performance measure to derive sets of data points for the servers, and performing regression on the sets of data points to yield power models for the servers. Multiple server power models may be created for different program applications.

Abstract: Methods, apparatus, and products as disclosed for estimating power consumption of computing components configured in a computing system that include: selecting, by a power estimation module, a plurality of calibration datasets from a calibration dataset repository, each calibration dataset specifying calibration power consumption by one or more computing components in the computing system for a calibration workload at a plurality of calibration operating points; measuring, by the power estimation module, a current power consumption by one or more measured computing components in the computing system for a current workload at a current operating point; determining, by the power estimation module, an estimated power consumption for the measured computing components at a proposed operating point in dependence upon the selected calibration datasets and the current power consumption for the current workload at the current operating point; and administering the computing system in dependence upon the estimated powe