A workload-aware energy model for virtual machine migration

Energy consumption has become a significant issue for data centres. Assessing their consumption requires precise and detailed models. In the latter years, many models have been proposed, but most of them either do not consider energy consumption related to virtual machine migration or do not consider the variation of the workload on (1) the virtual machines (VM) and (2) the physical machines hosting the VMs. In this paper, we show that omitting migration and workload variation from the models could lead to misleading consumption estimates. Then, we propose a new model for data centre energy consumption that takes into account the previously omitted model parameters and provides accurate energy consumption predictions for paravirtualised virtual machines running on homogeneous hosts. The new model's accuracy is evaluated with a comprehensive set of operational scenarios. With the use of these scenarios we present a comparative analysis of our model with similar state-of-the-art models for energy consumption of VM Migration, showing an improvement up to 24% in accuracy of prediction. © 2015 IEEE

The Overlooked Potential of Generalized Linear Models in Astronomy - I: Binomial Regression

Revealing hidden patterns in astronomical data is often the path to fundamental scientific breakthroughs; meanwhile the complexity of scientific inquiry increases as more subtle relationships are sought. Contemporary data analysis problems often elude the capabilities of classical statistical techniques, suggesting the use of cutting edge statistical methods. In this light, astronomers have overlooked a whole family of statistical techniques for exploratory data analysis and robust regression, the so-called Generalized Linear Models (GLMs). In this paper -- the first in a series aimed at illustrating the power of these methods in astronomical applications -- we elucidate the potential of a particular class of GLMs for handling binary/binomial data, the so-called logit and probit regression techniques, from both a maximum likelihood and a Bayesian perspective. As a case in point, we present the use of these GLMs to explore the conditions of star formation activity and metal enrichment in primordial minihaloes from cosmological hydro-simulations including detailed chemistry, gas physics, and stellar feedback. We predict that for a dark mini-halo with metallicity $\approx 1.3 \times 10^{-4} Z_{\bigodot}$, an increase of $1.2 \times 10^{-2}$ in the gas molecular fraction, increases the probability of star formation occurrence by a factor of 75%. Finally, we highlight the use of receiver operating characteristic curves as a diagnostic for binary classifiers, and ultimately we use these to demonstrate the competitive predictive performance of GLMs against the popular technique of artificial neural networks.Comment: 20 pages, 10 figures, 3 tables, accepted for publication in Astronomy and Computin

Dark Matter Halo Environment for Primordial Star Formation

We study the statistical properties (such as shape and spin) of high-z halos likely hosting the first (PopIII) stars with cosmological simulations including detailed gas physics. In the redshift range considered ($11 < z < 16$) the average sphericity is $= 0.3 \pm 0.1$, and for more than 90% of halos the triaxiality parameter is $T \lesssim 0.4$, showing a clear preference for oblateness over prolateness. Larger halos in the simulation tend to be both more spherical and prolate: we find $s \propto M_h^{\alpha_s}$ and $T \propto M_h^{\alpha_T}$, with $\alpha_s \approx 0.128$ and $\alpha_T= 0.276$ at z = 11. The spin distributions of dark matter and gas are considerably different at $z=16$, with the baryons rotating slower than the dark matter. At lower redshift, instead, the spin distributions of dark matter and gas track each other almost perfectly, as a consequence of a longer time interval available for momentum redistribution between the two components. The spin of both the gas and dark matter follows a lognormal distribution, with a mean value at z=16 of $=0.0184$, virtually independent of halo mass. This is in good agreement with previous studies. Using the results of two feedback models (MT1 and MT2) by McKee & Tan (2008) and mapping our halo spin distribution into a PopIII IMF, we find that at high-$z$ the IMF closely tracks the spin lognormal distribution. Depending on the feedback model, though, the distribution can be centered at $\approx 65 M_\odot$ (MT1) or $\approx 140 M_\odot$ (MT2). At later times, model MT1 evolves into a bimodal distribution with a second prominent peak located at $35-40 M_\odot$ as a result of the non-linear relation between rotation and halo mass. We conclude that the dark matter halo properties might be a key factor shaping the IMF of the first stars.Comment: 10 pages, 6 figures, accepted for publication in MNRA

Ceinfo cliente: manual do usuario

bitstream/CNPAT/7915/1/doc58.pd

Robust PCA and MIC statistics of baryons in early minihaloes

We present a novel approach, based on robust principal components analysis (RPCA) and maximal information coefficient (MIC), to study the redshift dependence of halo baryonic properties. Our data are composed of a set of different physical quantities for primordial minihaloes: dark matter mass (M-dm), gas mass (M-gas), stellar mass (M-star), molecular fraction (x(mol)), metallicity (Z), star formation rate (SFR) and temperature. We find that M-dm and M-gas are dominant factors for variance, particularly at high redshift. Nonetheless, with the emergence of the first stars and subsequent feedback mechanisms, x(mol), SFR and Z start to have a more dominant role. Standard PCA gives three principal components (PCs) capable to explain more than 97 per cent of the data variance at any redshift (two PCs usually accounting for no less than 92 per cent), whilst the first PC from the RPCA analysis explains no less than 84 per cent of the total variance in the entire redshift range (with two PCs explaining greater than or similar to 95 per cent anytime). Our analysis also suggests that all the gaseous properties have a stronger correlation with M-gas than with M-dm, while M-gas has a deeper correlation with x(mol) than with Z or SFR. This indicates the crucial role of gas molecular content to initiate star formation and consequent metal pollution from Population III and Population II/I regimes in primordial galaxies. Finally, a comparison between MIC and Spearman correlation coefficient shows that the former is a more reliable indicator when halo properties are weakly correlated

Short-range template switching in great ape genomes explored using pair hidden Markov models.

Many complex genomic rearrangements arise through template switch errors, which occur in DNA replication when there is a transient polymerase switch to an alternate template nearby in three-dimensional space. While typically investigated at kilobase-to-megabase scales, the genomic and evolutionary consequences of this mutational process are not well characterised at smaller scales, where they are often interpreted as clusters of independent substitutions, insertions and deletions. Here we present an improved statistical approach using pair hidden Markov models, and use it to detect and describe short-range template switches underlying clusters of mutations in the multi-way alignment of hominid genomes. Using robust statistics derived from evolutionary genomic simulations, we show that template switch events have been widespread in the evolution of the great apes' genomes and provide a parsimonious explanation for the presence of many complex mutation clusters in their phylogenetic context. Larger-scale mechanisms of genome rearrangement are typically associated with structural features around breakpoints, and accordingly we show that atypical patterns of secondary structure formation and DNA bending are present at the initial template switch loci. Our methods improve on previous non-probabilistic approaches for computational detection of template switch mutations, allowing the statistical significance of events to be assessed. By specifying realistic evolutionary parameters based on the genomes and taxa involved, our methods can be readily adapted to other intra- or inter-species comparisons

A multidisciplinary study on the spatial variability of the local stratigraphic conditions in partially saturated slopes for flow-like landslide prediction

Flow-like landslides, which occur mainly in shallow granular deposits resting on steep bedrock, represent a major natural hazard worldwide. The pore water pressure distribution and the soil water content directly affect the soil shear strength, thus controlling the triggering of these landslides. Criticalgeomorphological and topographical settings, together with peculiar stratigraphic and hydrogeological features, are commonly recognized as predisposing factors for flow-like landslides occurrence. Hence, investigating the spatial and temporal variability of hydraulic slope conditions is a fundamental activity that consists of identifying local geological factors and seasonal monitoring of the subsurface water regime. The present work proposes an integrated geological, geophysical and geotechnical approach to identify the spatial variability of the local stratigraphic setting and hydrogeological conditions in a partially saturated slope, in order to set up a procedure able to provide a prediction of the flow-like landslides occurrence atslope scale. The multidisciplinary study has been applied to a test site on Mt. Faito, in the Lattari Mts. (Southern Italy), where extensive geophysical, geological and geotechnical soil characterization and in situmonitoring data collected over two years are available

Muscle strength and balance as mediators in the association between physical activity and health-related quality of life in community-dwelling older adults

Lower extremity muscle strength (LEMS) and body balance (BB) are essential for older adults to maintain an upright posture and autonomously perform their basic activities of daily living. This study aimed to examine whether LEMS and BB mediate the relationship between physical activity (PA) and health-related quality of life (HRQoL) in a large sample of community-dwelling older adults. This is a cross-sectional study carried out with 802 individuals, 401 males and 401 females (69.8 ± 5.6 years), residents of the Autonomous Region of Madeira, Portugal. PA and HRQoL were assessed by the Baecke Questionnaire and e SF-36, respectively. LEMS was assessed by the Senior Fitness Test and BB by the Fullerton Advance Balance (FAB). The serial mediation pathway model pointed out that LEMS and BB partially mediated the association between PA and HRQoL in approximately 39.6% and 47%, respectively. The total variance in HRQoL explained by the entire model was 98%. Our findings may indicate the role that LEMS and BB play in the relationship between PA and HRQoL in the older population.info:eu-repo/semantics/publishedVersio