Active vibration control of a fluid/plate system

Cette thèse s’intéresse au problème du contrôle actif des vibrations structurelles d’une aile d’avion induites par le ballottement du carburant dans les réservoirs qu’elle contient. L'étude proposée ici est concentrée sur l'analyse d'un dispositif expérimental composé d'une longue plaque rectangulaire en aluminium équipée d'actionneurs et de capteurs piézoélectriques et d'un réservoir cylindrique. La difficulté principale réside dans le couplage complexe entre les modes de vibration de l’aile et les modes de ballottement du liquide. Un modèle de ce dispositif à l’aide d’équations aux dérivées partielles est tout d’abord construit. Ce modèle de dimension infinie couple une équation des plaques avec l'équation de Bernoulli pour le mouvement du fluide dans le réservoir. En analysant la contribution énergétique des modes, une approximation en dimension finie, de type espace d'état est alors construite. Après une méthode de recalage fréquentiel du modèle, un contrôle est réalisé en utilisant dans un premier temps une méthode par placement de pôle et dans un deuxième temps, la théorie de la commande robuste H-infini. La dimension du modèle et les performances demandées imposent le calcul d’un contrôleur H-infini d'ordre réduit, conçu en utilisant la librairie HIFOO 2.0 et testé sur le dispositif expérimental pour différents niveaux de remplissage. Finalement, le problème de la correction simultanée avec un correcteur HIFOO d'ordre réduit est aussi analysé.We consider the problem of the active reduction of structural vibrations of a plane wing induced by the sloshing of large masses of fuel inside partly full tank. This study focuses on an experimental device composed of an aluminum rectangular plate equipped with piezoelectric actuators/sensors at the clamped end and with a cylindrical tip-tank, more or less filled with liquid, at the opposite free end. The control is performed through piezoelectric actuators and the main difficulty comes from the complex coupling between the flexible modes of the wing and the sloshing modes of the fuel. First, a partial derivative equation model is computed by coupling a plate equation with a Bernoulli equation for the fluid motion. After analyzing the energetic contribution of each mode, a state space approximation is established. After a model matching procedure, a control is computed by using the pole placement method and the H-infinity theory. Due to the large scale of the synthesis model and to the simultaneous performance requirements, a reduced-order H-infinity controller is computed using the HIFOO 2.0 package and tested on the experimental device for different filling levels. Finally, the problem of simultaneous control with a reduced order HIFOO controller is tackled. Experimental results of this non-convex optimization problem are given and commented

Inequalities in health status of world population

Abstract: Objectives The paper aims to study the regional variation in population health at world level. It focuses on the analysis of the influence of determinant factors, such as geographic region and income, on health. Prior Work If previous studies on health refer to a specific group of countries, the paper expands the analysis of health status to world countries. Based on prior findings from the literature regarding the factors that affect health, the paper considers two main determinants, income and geographic region. Approach The health status of the population is assessed through a widely used indicator, namely life expectancy at birth, observed for a sample of 193 countries, in 2009. For the analysis of variation of life expectancy among world regions we apply the ANOVA and contrasts methods. We test the differences in life expectancy for different groups of countries. Results The results show that high income countries have the highest average life expectancy. Moreover, life expectancy in European countries is higher than American countries, while African countries have the lowest life expectancy compared to the rest of the world. Implications The existence of differences in life expectancy among world countries reveal the need for differentiated health policies in order to eradicate factors that have negative effects on population health. Value The paper allows to identify the regions that are best performers in health and to explain the differences in health between countries grouped by income level and geographic position

Modeling and Control of MapReduce Systems

posterInternational audienceSystems based on the MapReduce programming model are emerging as a central tool for deploying jobs that process large datasets in parallel. However the configuration of MapReduce systems is a complex process and at the moments it's left up to the user. These ad-hoc configuration methods make it difficult for small companies to take advantage of the growth of cloud computing solutions that provide resources as a service. Furthermore, the definition of SLAs becomes a complicated process for the user and the service provider as well. We propose a control theoretical approach to solving these problems. This implies the development of a general model that captures the dynamics of MapReduce systems. Finally, we intend to provide novel control methods that ease the configuration process and guarantee service level objectives such as constraints on system performance (execution times) and dependability (latency, availability) while optimizing resource consumption

Feedback Control for Online Training of Neural Networks

International audienc

Adaptive Modelling and Control in Distributed Systems

International audienceCompanies have growing amounts of data to store and to process. In response to these new processing challenges, Google developed MapReduce, a parallel programming paradigm which is becoming the major tool for BigData treatment. Even if MapReduce is used by most IT companies, ensuring its performances while minimizing costs is a real challenge requiring a high level of expertise. Modelling and control of MapReduce have been developed in the last years, however there are still many problems caused by the software's high variability. To tackle the latter issue, this paper proposes an on-line model estimation algorithm for MapReduce systems. An adaptive control strategy is developed and implemented to guarantee response time performances under a concurrent workload while minimizing resource use. Results have been validated using a 40 nodes MapReduce cluster under a data intensive Business Intelligence workload running on Grid5000, a French national cloud. The experiments show that the adaptive control algorithm manages to guarantee performances and low costs even in a highly variable environment

Feedback Autonomic Provisioning for Guaranteeing Performance in MapReduce Systems

International audienceCompanies have a fast growing amounts of data to process and store, a data explosion is happening next to us. Currentlyone of the most common approaches to treat these vast data quantities are based on the MapReduce parallel programming paradigm.While its use is widespread in the industry, ensuring performance constraints, while at the same time minimizing costs, still providesconsiderable challenges. We propose a coarse grained control theoretical approach, based on techniques that have already provedtheir usefulness in the control community. We introduce the first algorithm to create dynamic models for Big Data MapReduce systems,running a concurrent workload. Furthermore we identify two important control use cases: relaxed performance - minimal resourceand strict performance. For the first case we develop two feedback control mechanism. A classical feedback controller and an evenbasedfeedback, that minimises the number of cluster reconfigurations as well. Moreover, to address strict performance requirements afeedforward predictive controller that efficiently suppresses the effects of large workload size variations is developed. All the controllersare validated online in a benchmark running in a real 60 node MapReduce cluster, using a data intensive Business Intelligenceworkload. Our experiments demonstrate the success of the control strategies employed in assuring service time constraints

Application du contrôle pour garantir la performance des systèmes Big Data

International audienceNous sommes à l'aube d'une énorme explosion de données et la quantité à traiter par les entreprises est de plus en plus grande. Pour faire face à ce chalenge, Google a développé MapReduce, un modèle de programmation parallèle qui est en train de devenir l'outil de facto pour l'analyse des systèmes Big Data. Bien que dans une certaine mesure son utilisation est déjà très répandue dans l'industrie, garantir les performances d'un système aussi complexe pose de grands problèmes et sa gestion nécessite un haut niveau d'expertise. Cet article répond à ces défis en proposant le premier système autonome qui garantit des contraintes de temps de réponse pour une charge de travail MapReduce simultanée. Nous développons le premier modèle dynamique d'une grappe MapRe- duce. De plus, un contrôle en boucle fermée est conçu et implémenté pour garantir un temps de réponse donné. Un contrôle d'anticipation de type ""feedforward"" est également rajouté pour amé- liorer la réponse du système en présence de perturbations, en l'occurrence, la variation du nombre de clients. L'approche est validée en ligne sur une grappe MapReduce avec 40 nœuds utilisant une charge de travail intensive de type Business Intelligence. Nos expériences montrent que le contrôle ainsi conçu peut garantir les contraintes de temps de réponse

Adaptive Optimal Control of MapReduce Performance, Availability and Costs

International audienceMapReduce is a popular programming model for distributed data processing and Big Data applications running on clouds. Extensive research has been conducted either to improve the dependability or to increase performance of MapReduce, ranging from adaptive and on-demand fault-tolerance solutions, adaptive task scheduling techniques to optimized job execution mechanisms. This paper investigates an optimization-based solution to control MapReduce systems in order to provide guarantees in terms of both performance and availability while reducing utilization costs. We follow a control theoretical approach for MapReduce cluster scaling and admission control. Moreover, we aim to be robust to changes in MapRe-duce and in it's environment by adapting the controller online to those changes. This paper highlights the major challenges of combining system adaptation and optimal control to take the best of both approaches. CCS Concepts • Networks → Cloud computing; • Software and its engineering → Software configuration management and version control systems; • Computer systems organization → Dependable and fault-tolerant systems and networks

Cost Function based Event Triggered Model Predictive Controllers - Application to Big Data Cloud Services

International audienceHigh rate cluster reconfigurations is a costly issue in Big Data Cloud services. Current control solutions manage to scale the cluster according to the workload, however they do not try to minimize the number of system reconfigurations. Event-based control is known to reduce the number of control updates typically by waiting for the system states to degrade below a given threshold before reacting. However, computer science systems often have exogenous inputs (such as clients connections) with delayed impacts that can enable to anticipate states degradation. In this paper, a novel event-triggered approach is proposed. This triggering mechanism relies on a Model Predictive Controller and is defined upon the value of the optimal cost function instead of the state or output error. This controller reduces the number of control changes, in the normal operation mode, through constraints in the MPC formulation but also assures a very reactive behavior to changes of exogenous inputs. This novel control approach is evaluated using a model validated on a real Big Data system. The controller efficiently scales the cluster according to specifications, meanwhile reducing its reconfigurations

Towards Control of MapReduce Performance and Availability

International audienceMapReduce is a popular programming model for distributed data processing and Big Data applications. Extensive research has been conducted either to improve the dependability or to increase performance of MapReduce, ranging from adaptive and on-demand fault-tolerance solutions, adaptive task scheduling techniques to optimized job execution mechanisms. This paper investigates a novel solution that controls MapReduce systems and provides guarantees in terms of both performance and availability, while reducing utilization costs. We follow a control theoretic approach for MapReduce cluster scaling and admission control. Preliminary results based on a simulation environment, previously validated on a real MapReduce cluster, show the effectiveness of the proposed control solutions for a Hadoop MapReduce cluster