Building a Virtual Globus Grid in a Reconfigurable Environment - A case study: Grid5000

With the continuous evolution of distributed computing grids and with the perpetuous development of the available computing resources and protocols, there is a \textit{sine qua non} requirement to pass beyond the physical design of the grids. A viable solution is offered by virtual grids, having the advantage of flexible mapping and adaptation to live in-place resources. A software image is proposed, built with the use of the Globus Toolkit, the herein document describing the construction and configuratin phases as well as the deployment protocol in a live grid - Grid5000

Predictive Modeling in a VoIP System, Journal of Telecommunications and Information Technology, 2013, nr 4

An important problem one needs to deal with in a Voice over IP system is server overload. One way for preventing such problems is to rely on prediction techniques for the incoming traffic, namely as to proactively scale the available resources. Anticipating the computational load induced on processors by incoming requests can be used to optimize load distribution and resource allocation. In this study, the authors look at how the user profiles, peak hours or call patterns are shaped for a real system and, in a second step, at constructing a model that is capable of predicting trends

EXPERIMENTS WITH SOUNDS IN REPELLING MAMMALS

Since its introduction for use in repelling birds , a number of people have found that Av-Alarm is effective for control of certain mammals. This includes not only those familiar to North Americans (deer, elk , coyotes), but also various less familiar species, even anthropoids (baboons) and bats. A number of example cases are described. A concept theory is presented in order to explain why certain sounds are more effective than others, and why sounds originally meant for bird control are also effective with mammals. The theory helps to predict untested situations , and also suggests when complex repelling sounds can profitably be augmented by other sounds or by visual harassment

Métaheuristiques parallèles hybrides pour le docking moléculaire de protéines sur grilles de calcul

Cette thèse porte sur les méta-heuristiques hiérarchiques parallèles adaptatives pour l'échantillonnage conformationnel. Étant un problème hautement combinatoire et multlmodal, l'échantillonnage conformationnel requière la construction d'approches hybrides à large échelle. Après une analyse dei modèles mathématiques, nécessitant l'examen des différentes formulations du champ de force, nous avons proposé une étude des opérateurs de variation et des méthodes de recherche locale adaptés au problème ainsi que leur hybridation dynamique et adaptative. Cette étude nous a conduit à la proposition de mécanismes d'adaptation des paramètres des algorithmes utilisés en fonction du processus d'évolution. Dans cette thèse, nous proposons également des algorithmes adaptatifs hybndes hiérarchiques distribués, fortement extensibles. L'expérimentation, basée sur l'utilisation de multiples modèles parallèles, démontre la grande efficacité de ces algorithmes. En effet, les résultats obtenus montrent que des RMSD moyens en dessous de 1.0 A peuvent être obtenus sur des instances difficiles des problèmes de prédiction de la structure des protéines et de docking moléculaire. La validation des approches hybrides proposées a été effectuée sur Grid'5000, une grille expérimentale d'échelle nationale composée d'environ 5000 coeurs de calcul. Une image système a été développée en utilisant Globus pour permettre des déploiements distribués à large échelle. L'approche hiérarchique distribuée construite a été ainsi déployée sur plusieurs grappes, avec près de 1000 coeurs de calcul.The thesis proposes an extensive analysis of adaptive hierarchical parallel metaheuristics for ab initio conformational sampling. Standing as an NP, combinatorial, highly multi-modal optimization problem, conformational sampling requires for high-performance large scale hybrid approaches to be constructed. Following an incremental definition, minimum complexity conformational sampling mathematical models are first analyzed, entailing a review of different force field formulations. A comprehensive analysis is conducted on a large set of operators and local search algorithms including adaptive and dynamic mechanisms. As determined by the analysis outcomes, complex a priori and online parameter tuning stages are designed. finally, highly scalable hierarchical hybrid distributed algorithm designs are proposed. Experimentation is carried over multiple parallelization models with afferent cooperation topologies. Expenmentations resulted in unprecedented results to be obtained. Multiple perfect conformational matches have been determined, on highly difficult protein structure prediction and molecular docking benchmarks, with RMSD average values below 1.0A. The validation of the proposed hybrid approaehes was performed on Grid'5000, a French computational grid, with almost 5000 computational cores. A Globus Toolkit hased Grid'SOOO system image has been developed, sustaining large scale distributed deployments. The constructed hierarchical hybrid distributed algorithm has been deployed on multiple clusters, with almost 1000 computing cores. Finally, a parallel AutoDock version was developed using the ParadisEO framework, integrating the developed algorithms

Asymmetric quadratic landscape approximation model

This work presents an asymmetric quadratic approximation model and an ε-archiving algorithm. The model allows to construct, under local convexity assumptions, descriptors for local optima points in continuous functions. A descriptor can be used to extract confidence radius information. The ε-archiving algorithm is designed to maintain and update a set of such asymmetric descriptors, spaced at some given threshold distance. An in-depth analysis is conducted on the stability and performance of the asymmetric model, comparing the results with the ones obtained by a quadratic polynomial approximation. A series of different applications are possible in areas such as dynamic and robust optimization. © 2014 ACM

On dynamic multi-objective optimization - classification and performance measures

In this work we focus on defining how dynamism can be modeled in the context of multi-objective optimization. Based on this, we construct a component oriented classification for dynamic multi-objective optimization problems. For each category we provide synthetic examples that depict in a more explicit way the defined model. We do this either by positioning existing synthetic benchmarks with respect to the proposed classification or through new problem formulations. In addition, an online dynamic MNK-landscape formulation is introduced together with a new comparative metric for the online dynamic multi-objective context

On the Foundations and the Applications of Evolutionary Computing

Genetic type particle methods are increasingly used to sample from complex high-dimensional distributions. They have found a wide range of applications in applied probability, Bayesian statistics, information theory, and engineering sciences. Understanding rigorously these new Monte Carlo simulation tools leads to fascinating mathematics related to Feynman-Kac path integral theory and their interacting particle interpretations. In this chapter, we provide an introduction to the stochastic modeling and the theoretical analysis of these particle algorithms. We also illustrate these methods through several applications

Sparse Antenna Array Optimization with the Cross-Entropy Method

The interest in sparse antenna arrays is growing, mainly due to cost concerns, array size limitations, etc. Formally, it can be shown that their design can be expressed as a constrained multidimensional nonlinear optimization problem. Generally, through lack of convex property, such a multiextrema problem is very tricky to solve by usual deterministic optimization methods. In this article, a recent stochastic approach, called Cross-Entropy method, is applied to the continuous constrained design problem. The method is able to construct a random sequence of solutions which converges probabilistically to the optimal or the near-optimal solution. Roughly speaking, it performs adaptive changes to probability density functions according to the Kullback-Leibler cross-entropy. The approach efficiency is illustrated in the design of a sparse antenna array with various requirements