Modelling Energy Consumption based on Resource Utilization

Power management is an expensive and important issue for large computational infrastructures such as datacenters, large clusters, and computational grids. However, measuring energy consumption of scalable systems may be impractical due to both cost and complexity for deploying power metering devices on a large number of machines. In this paper, we propose the use of information about resource utilization (e.g. processor, memory, disk operations, and network traffic) as proxies for estimating power consumption. We employ machine learning techniques to estimate power consumption using such information which are provided by common operating systems. Experiments with linear regression, regression tree, and multilayer perceptron on data from different hardware resulted into a model with 99.94\% of accuracy and 6.32 watts of error in the best case.Comment: Submitted to Journal of Supercomputing on 14th June, 201

Additional file 7: Figure S7. of Longitudinal profiling reveals a persistent intestinal dysbiosis triggered by conventional anti-tuberculosis therapy

Repeat experiment demonstrating reproducibility of key findings observed during treatment. See Additional file 6: Figure S6 for description with the following exceptions: C57BL/6J female mice were used and treatment was terminated at month 7 of Mtb infection, and mice were not monitored post cessation of therapy. n = 3–5. (PDF 657 kb

Additional file 6: Figure S6. of Longitudinal profiling reveals a persistent intestinal dysbiosis triggered by conventional anti-tuberculosis therapy

Repeat experiment demonstrating reproducibility of major differences observed during as well as post treatment. a Outline of experimental plan for longitudinal analysis of alterations in the microbiota induced by ATT in Mtb-infected C57BL/6J-CD45a(Ly5a) female mice. Two groups of mice (TB and TB + HRZ) were employed with each group consisting of four animals. Stool sample collection time points are indicated as colored circles (TB, red; TB + HRZ, orange). For the purpose of consistency, the time points shown refer to the month (M) of stool sample collection relative to the date of infection rather than treatment. In the case of the TB + HRZ group, treatment was ceased at M5 and post HRZ samples (yellow circles) were collected at M8. H, Isoniazid; R, Rifampin; Z, Pyrazinamide. b Community diversity in the TB and TB + HRZ animal groups for every stool sample collected was calculated from 16S sequences using Chao1 (left) and Shannon (right) indices. Error bars indicate maximum and minimum values. Significance tests were performed between the corresponding time points in the two groups. *p < 0.05, Wilcoxon-rank sum test. c Principal coordinate (PC) analysis of unweighted (left) and weighted (right) UniFrac distances of the sequences from the animal groups. Each sphere represents a single animal with the size of the sphere referring to the sample collection time point (early to late time points indicated as a gradient in the size of the spheres from small to large). d LEfSe analysis was performed to identify genera that are differentially abundant between the TB and TB + HRZ groups. Taxa significantly enriched in the TB or TB + HRZ groups depicted with red or orange bars, respectively. Data are filtered for p < 0.01 and LDA score >2. n = 4. (PDF 719 kb

Additional file 10: Figure S10. of Longitudinal profiling reveals a persistent intestinal dysbiosis triggered by conventional anti-tuberculosis therapy

Replicate experiment utilizing Mtb-infected mice for comparison of single and multi-drug effects on the microbiota. a Nine groups of mice with 3–4 animals in each group were employed. One group was left uninfected and untreated as the naïve age-matched control, and the remaining eight groups were infected with Mtb (aerosol). Four weeks after infection, seven of the infected groups were each treated with one or a combination of H (Isoniazid), R (Rifampin), and/or Z (Pyrazinamide) as indicated and separated by a ‘/’. b Bacterial community diversity of all the samples in each group was estimated using alpha diversity indices Chao1 (top) and Shannon (bottom). Error bars indicate maximum and minimum values. *p < 0.05; **p < 0.01; ***p < 0.001; ****p < 0.0001, Welch’s t test. Blue and red asterisks indicate significance in comparison to Naïve and TB groups, respectively. c Principal coordinate (PC) analysis of unweighted UniFrac distances of sequences from all nine groups. d Heat map showing the average species level relative abundance. Data shown are filtered for an overall relative variance >10 and depicted as described in Fig. 2c except along the x-axis, which shows the different treatment groups. Naïve, TB, HRZ, n = 3; remaining groups n = 4. (PDF 655 kb

Additional file 13: Figure S13. of Longitudinal profiling reveals a persistent intestinal dysbiosis triggered by conventional anti-tuberculosis therapy

Unweighted and weighted UniFrac analysis of the sequences from the four groups described in Additional file 1: Figure S1. Each sphere represents a single animal and all animals from all time points were included in this analysis including the samples excluded in Figs. 1b, 2b, and 5b. The size of the sphere increases with respect to time. n = 4–5 for each time point except W20 time point of TB group where n = 3. (PDF 1103 kb

Additional file 2: Figure S2. of Longitudinal profiling reveals a persistent intestinal dysbiosis triggered by conventional anti-tuberculosis therapy

Analysis of bacterial community diversity in the experimental groups shown in Figure S1. a–c Alpha diversity estimates as calculated by Chao1 (left panel) and Shannon (right panel) indices from the 16S sequence data for each of the time points in the naïve and TB (W1–W20) (a) naïve, TB, and TB + HRZ (W4–W20) (b) and naïve (W24–W32) and post HRZ (W24–W32) (c). The experimental groups are indicated along the x-axes. The bars indicate the mean ± SEM for each animal group in the comparison. Statistical significance between the groups based on pooled data from all time points of each group was calculated using a non-parametric t test with 999 Monte-Carlo permutations. (PDF 408 kb