Energy-aware dynamic pricing model for cloud environments

Energy consumption is a critical operational cost for Cloud providers. However, as commercial providers typically use fixed pricing schemes that are oblivious about the energy costs of running virtual machines, clients are not charged according to their actual energy impact. Some works have proposed energy-aware cost models that are able to capture each client’s real energy usage. However, those models cannot be naturally used for pricing Cloud services, as the energy cost is calculated after the termination of the service, and it depends on decisions taken by the provider, such as the actual placement of the client’s virtual machines. For those reasons, a client cannot estimate in advance how much it will pay. This paper presents a pricing model for virtualized Cloud providers that dynamically derives the energy costs per allocation unit and per work unit for each time period. They account for the energy costs of the provider’s static and dynamic energy consumption by sharing out them according to the virtual resource allocation and the real resource usage of running virtual machines for the corresponding time period. Newly arrived clients during that period can use these costs as a baseline to calculate their expenses in advance as a function of the number of requested allocation and work units. Our results show that providers can get comparable revenue to traditional pricing schemes, while offering to the clients more proportional prices than fixed-price models.Peer ReviewedPostprint (author's final draft

Radial variations in the stellar initial mass function of early-type galaxies

The hypothesis of a universal initial mass function (IMF) -- motivated by observations in nearby stellar systems -- has been recently challenged by the discovery of a systematic variation of the IMF with the central velocity dispersion, {\sigma}, of early-type galaxies (ETGs), towards an excess of low-mass stars in high-{\sigma} galaxies. This trend has been derived so far from integrated spectra, and remains unexplained at present. To test whether such trend depends on the local properties within a galaxy, we have obtained new, extremely deep, spectroscopic data, for three nearby ETGs, two galaxies with high {\sigma} (~300 km/s), and one lower mass system, with {\sigma} ~ 100 km/s. From the analysis of IMF-sensitive spectral features, we find that the IMF depends significantly on galactocentric distance in the massive ETGs, with the enhanced fraction of low-mass stars f mostly confined to their central regions. In contrast, the low-{\sigma} galaxy does not show any significant radial gradient in the IMF, well described by a shallower distribution, relative to the innermost regions of massive galaxies, at all radii. Such a result indicates that the IMF should be regarded as a local (rather than global) property, and suggests a significant difference between the formation process of the core and the outer regions of massive ETGs.Comment: 17 pages, 13 figures. Accepted for publication in MNRA

Timing the formation and assembly of early-type galaxies via spatially resolved stellar populations analysis

To investigate star formation and assembly processes of massive galaxies, we present here a spatially-resolved stellar populations analysis of a sample of 45 elliptical galaxies (Es) selected from the CALIFA survey. We find rather flat age and [Mg/Fe] radial gradients, weakly dependent on the effective velocity dispersion of the galaxy within half-light radius. However, our analysis shows that metallicity gradients become steeper with increasing galaxy velocity dispersion. In addition, we have homogeneously compared the stellar populations gradients of our sample of Es to a sample of nearby relic galaxies, i.e., local remnants of the high-z population of red nuggets. This comparison indicates that, first, the cores of present-day massive galaxies were likely formed in gas-rich, rapid star formation events at high redshift (z>2). This led to radial metallicity variations steeper than observed in the local Universe, and positive [Mg/Fe] gradients. Second, our analysis also suggests that a later sequence of minor dry mergers, populating the outskirts of early-type galaxies (ETGs), flattened the pristine [Mg/Fe] and metallicity gradients. Finally, we find a tight age-[Mg/Fe] relation, supporting that the duration of the star formation is the main driver of the [Mg/Fe] enhancement in massive ETGs. However, the star formation time-scale alone is not able to fully explain our [Mg/Fe] measurements. Interestingly, our results match the expected effect that a variable stellar initial mass function would have on the [Mg/Fe] ratio.Comment: Accepted for publication in MNRA

BDGS: A Scalable Big Data Generator Suite in Big Data Benchmarking

Data generation is a key issue in big data benchmarking that aims to generate application-specific data sets to meet the 4V requirements of big data. Specifically, big data generators need to generate scalable data (Volume) of different types (Variety) under controllable generation rates (Velocity) while keeping the important characteristics of raw data (Veracity). This gives rise to various new challenges about how we design generators efficiently and successfully. To date, most existing techniques can only generate limited types of data and support specific big data systems such as Hadoop. Hence we develop a tool, called Big Data Generator Suite (BDGS), to efficiently generate scalable big data while employing data models derived from real data to preserve data veracity. The effectiveness of BDGS is demonstrated by developing six data generators covering three representative data types (structured, semi-structured and unstructured) and three data sources (text, graph, and table data)

DC-Prophet: Predicting Catastrophic Machine Failures in DataCenters

When will a server fail catastrophically in an industrial datacenter? Is it possible to forecast these failures so preventive actions can be taken to increase the reliability of a datacenter? To answer these questions, we have studied what are probably the largest, publicly available datacenter traces, containing more than 104 million events from 12,500 machines. Among these samples, we observe and categorize three types of machine failures, all of which are catastrophic and may lead to information loss, or even worse, reliability degradation of a datacenter. We further propose a two-stage framework-DC-Prophet-based on One-Class Support Vector Machine and Random Forest. DC-Prophet extracts surprising patterns and accurately predicts the next failure of a machine. Experimental results show that DC-Prophet achieves an AUC of 0.93 in predicting the next machine failure, and a F3-score of 0.88 (out of 1). On average, DC-Prophet outperforms other classical machine learning methods by 39.45% in F3-score.Comment: 13 pages, 5 figures, accepted by 2017 ECML PKD

La formación en economía social. Análisis de la oferta universitaria de posgrado en España

En el presente artículo se analiza la importancia que tiene la formación para el fomento de la economía social, prestando especial atención a la caracterización de la oferta formativa de este tipo de estudios y diferenciándola respecto a la oferta formativa convencional en el área de Administración y Dirección de Empresas, emprendimiento e innovación. Previo a ello, se reflexiona, en base a un breve análisis bibliográfico, sobre las relaciones que se establecen entre educación y economía para, posteriormente, centrarnos en la caracterización de este tipo de formación, prestando especial atención a la que se centra en la Economía Social. Para ello, en primer lugar, se ha elaborado una base de datos en la que se incluyen todas las titulaciones de posgrado relacionadas con los estudios de Economía. Asimismo, se ha diseñado un cuestionario estructurado con el objetivo de caracterizar este tipo de formación. Se concluye la escasa importancia que tiene en el sistema universitario de posgrado español la formación en Economía Social, detectándose algunas diferencias significativas en la caracterización del proceso formativo entre las titulaciones de Economía Social y las convencionales de Administración y Dirección de Empresas, emprendimiento e innovación empresarial, en aspectos tales, como: los valores, las competencias, las habilidades, la forma de entender y transmitir el funcionamiento de la economía, etc. A partir de estas conclusiones se dan una serie de recomendaciones que pueden contribuir a fomentar y dinamizar, mediante la formación en posgrados, la Economía Social

Aligning rTWT with 802.1Qbv: a Network Calculus Approach

Industry 4.0 applications impose the challenging demand of delivering packets with bounded latencies via a wireless network. This is further complicated if the network is not dedicated to the time critical application. In this paper we use network calculus analysis to derive closed form expressions of latency bounds for time critical traffic when 802.11 Target Wake Time (TWT) and 802.1Qbv work together in a shared 802.11 network