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In this paper we prove a stability result for Hamilton-Jacobi equations with an integro-differential term for discontinuous Hamiltonians. This type of equations arises in various problems concerning, for example, the control of diffusion processes with jumps, the theory of large deviations for processes with jumps, and the theory of piecewise deterministic processes. 1. Introduction. The theory of viscosity solutions of Hamilton-Jacobi equations, introduced by M. G. Crandall and P. L. Lions [5], has given rise to numerous developments in such a way that from now on it covers all necessary tools for an effective use in applied mathematics. The case of Hamilton-Jacobi equations with continuous Hamiltonians was simplified by M. G. Crandall, L. C. Evans, and P. L. Lions [6] and thereafter extended, by P. L. Lions [8], to second order equations. The extension, of the notion of viscosity solutions to noncontinuous first order Hamiltonians, was first obtained by H. Ishii [4], and later by G. Barles and B. Perthame [2]. Short time after, A. Sayah [9] proved existence and uniqueness of viscosity solutions for the continuous Hamilton-Jacobi equations with an integro-differential term.In this paper we intend to prove a stability result for Hamilton-Jacobi equations with an integro-differential term for discontinuous Hamiltonians H defined by:

This paper deals with the reduction of energy consumption in large scale systems, especially by taking into account the impact of energy consumption for server consolidation. Decreasing the number of physical hosts used while ensuring a certain level of quality of services is the goal of our approach. We introduce a metric called energetic yield which represents the quality of a task placement on a subset of machines, while taking into account quality of service and energy efficiency aspects. It measures the difference between resources required by a job and what the system allocates ultimately, while trying to save energy. Our work aims at minimizing this difference. We propose placement heuristics that are compared to the optimal solution and to a related system. In this paper, we present a set of experiments showing the relevance of this metric in order to reduce significantly energy consumption.

Abstract. The energy consumption of a computing system depends not only on its architecture, but also on its usage. This paper describes the Energy Consumption Library (libec), a modular library of sensors and power estimators, which do not depend on wattmeter to measure the power dissipated by a machine and/or the applications that it executes, etc. In addition, four use cases are used to demonstrate some of the library's capabilities.

In this paper, we propose a new robust tail index estimation procedure for Pareto-type distributions in the framework of randomly censored samples, based on the ideas of Kaplan-Meier integration using the huberized M-estimator of the tail index. We derive their asymptotic results. We illustrate the performance and the robustness of this estimator for small and large sample size in a simulation study. Résumé. Dans cet article, nous proposons une nouvelle procédure de l'estimation robuste de l'indice de la queue pour les distributions de type Pareto dans le cas d'échantillons censurés, sur la base des idées de l'intgrale de Kaplan-Meier en utilisant le huberized M-estimateur de l'indice de la queue. Nous dérivons leurs résultats asymptotiques. Nous illustrons dans l'étude de la simulation la performance et la robustesse de cet estimateur pour unéchantillon de petite et grande taille.

Kernel estimators of both density and regression functions are not consistent near the finite end points of their supports. In other words, boundary effects seriously affect the performance of these estimators. In this paper, we combine the transformation and the reflection methods in order to introduce a new general method of boundary correction when estimating the mean function. The asymptotic mean squared error of the proposed estimator is obtained. Simulations show that our method performes quite well with respect to some other existing methods. Résumé. Les estimateursà noyau des fonctions de densité et de régression présentent des problèmes de convergence aux bords de leurs supports. En d'autre termes, les effets de bord affectent sérieusement les performances de leurs estimateurs. Dans cet article, nous combinons les méthodes de transformation et de réflexion, pour introduire une nouvelle méthode générale de correction de l'effet de bord lors de l'estimation de la moyenne. L'erreur quadratique moyenne asymptotique de l'estimateur proposé est obtenue. Les simulations montrent que notre méthode se comporte assez bien par rapportà d'autres méthodes existantes.

ICAS 1: AUTONOMICInternational audienceNowadays, medium or large-scale distributed infrastructures such as clusters and grids are widely used to host various kinds of applications (e.g. web servers or scientific applications). Resource management is a major challenge for most organizations that run these infrastructures. Many studies show that clusters are not used at their full capacity and that there are therefore a huge source of waste. Autonomic management systems have been introduced in order to dynamically adapt software infrastructures according to runtime conditions. They provide support to deploy, configure, monitor, and repair applications in such environments. In this paper, we report our experiments in using an autonomic management system to provide resource aware management for a clustered application. We consider a standard replicated server infrastructure in which we dynamically adapt the degree of replication in order to ensure a given response time while minimizing energy consumption