Data centres are quickly evolving to support new demands for Cloud-Computing services. Extreme workload peaks represent a challenge for the maintenance of the performance and service level agreements, even more when operation costs need to be minimised. In this paper, we first present an extensive analysis of the impact of extreme workloads in large-scale realistic Cloud-Computing data centres, including a comparison between the most relevant centralised resourcemanaging models. Moreover, we extend our previous works by proposing a new energy-efficiency policy called Bullfighter which is able to keep performance key performance indicators while reducing energy consumption in extreme scenarios. This policy employs queue-theory distributions to foresee workload demands and adapt automatically to workload fluctuations even in extreme environments, while avoiding the fine-tuning required for other energy policies. Finally, it is shown through extensive simulation that Bullfighter can save more than 40% of energy in the aforementioned scenarios without exerting any noticeable impact on data-centre performance.
In order to model the variable T (the age of citations received by scientific works) with data elaborated by the Institute of Scientific Information, we have used some of the instruments already developed in the survival models to this type of retrospective analyses in the presence of censored data. This analysis is used because, usually, the citations of ages greater than or equal to 10 years appear added together. For a set of journals related to the field of Applied Economics, we have explored which models fit better among those commonly used. Two different approaches to assess the goodness-of-fit for each selected model have been suggested: an analysis through graphical methods and a formal analysis to estimate the parameters of each model by the method of maximum likelihood estimation with data censored to the right.
ResumenEl estudio de la variabilidad en caracteres categóricos rara vez es abordado. A partir de un enfoque menos usado de la variabilidad en variables cuantitativas, el de la disparidad, distinto al de la dispersión que, por ejemplo, proporciona la varianza, se propone la construcción de dos coeficientes de medida de la variabilidad en variables cualitativas o categóricas a los que llamamos coeficientes de disparidad. La sencillez y proximidad de los mismos permiten que sean abordados en un curso introductorio de estadística descriptiva. Ejemplos sencillos son ofrecidos para introducir las medidas y para, también, que el profesor constate la idea que el alumno tiene sobre variabilidad, dispersión y disparidad. Palabras clave:Variables cualitativas o categóricas; Variabilidad; Dispersión; Disparidad. AbstractThe study of variability in categorical characteristics is rarely discussed. From a less used viewpoint of variability in quantitative variables, as it is the one of dissimilarity, which is different from the dispersion that, for example, the variance provides, we propose the construction of two coefficients that measure the variability in qualitative or categorical variables, which we call
The main goal of a scientific journal is to diffuse new knowledge. The number of citations received by a journal can be considered as a measure of this objective and, in turn, as a measure of productivity in relation to the production process in which the journals are involved. In order to assess this production process, in this paper econometric models using data panel are employed to obtain measures of efficiency for those journals belonging simultaneously to the areas of “economics” and “social science, mathematical methods” in the Web of Science database. This efficiency is measured in terms of the distance between the actual production of the journals and their estimated maximum achievable number of citations based on their available resources.
El uso del método de máxima verosimilitud para estimar modelos de producción Half-Normal con frontera estocástica conlleva algunas dificultades prácticas que tal vez no han sido suficientemente enfatizadas. Usando el software FRONTIER, analizamos el caso en que la estimación sugiere la ausencia de factores aleatorios en el término de error compuesto. Hemos comprobado que existen motivos para pensar que las estimaciones de los parámetros y, sobre todo, sus errores estándar son de dudosa validez. El software LIMDEP no obtiene estimaciones en este caso, ofreciendo un mensaje de error.
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