An analytical methodology to derive power models based on hardware and software metrics

The use of models to predict the power consumption of a system is an appealing alternative to wattmeters since they avoid hardware costs and are easy to deploy. In this paper, we present an analytical methodology to build models with a reduced number of features in order to estimate power consumption at node level. We aim at building simple power models by performing a per-component analysis (CPU, memory, network, I/O) through the execution of four standard benchmarks. While they are executed, information from all the available hardware counters and resource utilization metrics provided by the system is collected. Based on correlations among the recorded metrics and their correlation with the instantaneous power, our methodology allows (i) to identify the significant metrics; and (ii) to assign weights to the selected metrics in order to derive reduced models. The reduction also aims at extracting models that are based on a set of hardware counters and utilization metrics that can be obtained simultaneously and, thus, can be gathered and computed on-line. The utility of our procedure is validated using real-life applications on an Intel Sandy Bridge architecture

MMP-2 and sTNF-R1 variability in patients with essential hypertension: 1-year follow-up study

The aim of this study is to analyze MMP-2 and sTNF-R1 variability, potent predictors of cardiovascular events, in stable hypertensive patients during a 12-month followup. 234 asymptomatic patients (age 6 0 ± 1 3 , 136 male) out of 252 patients with essential hypertension were followed up. MMP-2 and sTNF-R1 were measured at baseline and after 12 months (stage I). To compare MMP-2 and sTNF-R1 levels over time interval, we used the statistical method of Bland-Altman. MMP-2 and sTNF-R1 reproducibility was good in our patients for the two intervals with a coefficient of reproducibility of 8.2% and 11.3%, respectively. The percentages of patients within 1.96 × standard deviation of the mean were 93.6% and 92.7%. An elevated coefficient of correlation was obtained for MMP-2, basal versus stage I