13 research outputs found

    Advances in Grid Computing

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    This book approaches the grid computing with a perspective on the latest achievements in the field, providing an insight into the current research trends and advances, and presenting a large range of innovative research papers. The topics covered in this book include resource and data management, grid architectures and development, and grid-enabled applications. New ideas employing heuristic methods from swarm intelligence or genetic algorithm and quantum encryption are considered in order to explain two main aspects of grid computing: resource management and data management. The book addresses also some aspects of grid computing that regard architecture and development, and includes a diverse range of applications for grid computing, including possible human grid computing system, simulation of the fusion reaction, ubiquitous healthcare service provisioning and complex water systems

    Active Data: A Programming Model to Manage Data Life Cycle Across Heterogeneous Systems and Infrastructures

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    International audienceThe Big Data challenge consists in managing, storing, analyzing and visualizing these huge and ever growing data sets to extract sense and knowledge. As the volume of data grows exponentially, the management of these data becomes more complex in proportion. A key point is to handle the complexity of the data life cycle, i.e. the various operations performed on data: transfer, archiving, replication, deletion, etc. Indeed, data-intensive applications span over a large variety of devices and e-infrastructures which implies that many systems are involved in data management and processing. We propose Active Data, a programming model to automate and improve the expressiveness of data management applications. We first define the concept of data life cycle and introduce a formal model that allows to expose data life cycle across heterogeneous systems and infrastructures. The Active Data programming model allows code execution at each stage of the data life cycle: routines provided by programmers are executed when a set of events (creation, replication, transfer, deletion) happen to any data. We implement and evaluate the model with four use cases: a storage cache to Amazon-S3, a cooperative sensor network, an incremental implementation of the MapReduce programming model and automated data provenance tracking across heterogeneous systems. Altogether, these scenarios illustrate the adequateness of the model to program applications that manage distributed and dynamic data sets. We also show that applications that do not leverage on data life cycle can still benefit from Active Data to improve their performances

    Contributions to Desktop Grid Computing : From High Throughput Computing to Data-Intensive Sciences on Hybrid Distributed Computing Infrastructures

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    Since the mid 90’s, Desktop Grid Computing - i.e the idea of using a large number of remote PCs distributed on the Internet to execute large parallel applications - has proved to be an efficient paradigm to provide a large computational power at the fraction of the cost of a dedicated computing infrastructure.This document presents my contributions over the last decade to broaden the scope of Desktop Grid Computing. My research has followed three different directions. The first direction has established new methods to observe and characterize Desktop Grid resources and developed experimental platforms to test and validate our approach in conditions close to reality. The second line of research has focused on integrating Desk- top Grids in e-science Grid infrastructure (e.g. EGI), which requires to address many challenges such as security, scheduling, quality of service, and more. The third direction has investigated how to support large-scale data management and data intensive applica- tions on such infrastructures, including support for the new and emerging data-oriented programming models.This manuscript not only reports on the scientific achievements and the technologies developed to support our objectives, but also on the international collaborations and projects I have been involved in, as well as the scientific mentoring which motivates my candidature for the Habilitation `a Diriger les Recherches

    Active Data : Un modèle pour représenter et programmer le cycle de vie des données distribuées

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    National audienceAlors que la science génère et traite des ensembles de données toujours plus grands et dynamiques, un nombre croissant de scientifiques doit faire face à des défis pour permettre leur exploitation. La gestion de données par les applications scientifiques de traitement intensif des données requière le support de cycles de vie très complexes, la coordination de nombreux sites, de la tolérance aux pannes et de passer à l'échelle sur des dizaines de sites avec plusieurs péta-octets de données. Dans cet article, nous proposons un modèle pour représenter formellement les cycles de vie des applications de traitement de données et un modèle de programmation pour y réagir dynamiquement. Nous discutons du prototype d'implémentation et présentons différents cas d'études d'applications qui démontrent la pertinence de notre approche

    Active Data: A Data-Centric Approach to Data Life-Cycle Management

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    International audienceData-intensive science offers new opportunities for innovation and discoveries, provided that large datasets can be handled efficiently. Data management for data-intensive science applications is challenging; requiring support for complex data life cycles, coordination across multiple sites, fault tolerance, and scalability to support tens of sites and petabytes of data. In this paper, we argue that data management for data-intensive science applications requires a fundamentally different management approach than the current ad-hoc task centric approach. We propose Active Data, a fundamentally novel paradigm for data life cycle management. Active Data follows two principles: data-centric and event-driven. We report on the Active Data programming model and its preliminary implementation, and discuss the benefits and limitations of the approach on recognized challenging data-intensive science use-cases.Les importants volumes de données produits par la science présentent de nouvelles opportunités d'innovation et de découvertes. Cependant ceci sera conditionné par notre capacité à gérer efficacement de très grands jeux de données. La gestion de données pour les applications scientifiques data-intensive présente un véritable défi~; elle requière le support de cycles de vie très complexes, la coordination de plusieurs sites, de la tolérance aux pannes et de passer à l'échelle sur des dizaines de sites avec plusieurs péta-octets de données. Dans cet article nous argumentons que la gestion des données pour les applications scientifiques data-intensive nécessite une approche fondamentalement différente de l'actuel paradigme centré sur les tâches. Nous proposons Active Data, un nouveau paradigme pour la gestion du cycle de vie des données. Active Data suit deux principes~: il est centré sur les données et à base d'événements. Nous présentons le modèle de programmation Active Data, un prototype d'implémentation et discutons des avantages et limites de notre approche à partir d'étude de cas d'applications scientifiques

    Intermediate QoS Prototype for the EDGI Infrastructure

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    This document provides the first deliverable of EDGI JRA2. It is produced by the INRIA team, the SZTAKI team, the LAL/IN2P3 team and the University of Coimbra team. This document aims at describing achievements and results of JRA2 tasks "Advanced QoS Scheduler and Oracle" and "Support In Science Gateway". Hybrid Distributed Computing Infrastructures (DCIs) allow users to combine Grids, Desktop Grids, Clouds, etc. to obtain for their users large computing capabilities. The EDGI infrastructure belongs to this kind of DCIs. The document presents the SpeQuloS framework to provide quality of service (QoS) for application executed on the EDGI infrastructure. It also introduces EDGI QoS portal, an user-friendly and integrated access to QoS features for users of EDGI infrastructure. In this document, we first introduce new results from JRA2.1 task, which collected and analyzed batch execution on Desktop Grid. Then, we present the advanced Cloud Scheduling and Oracle strategies designed inside the SpeQuloS framework (task JRA2.2). We demonstrate efficiency of these strategies using performance evaluation carried out with simulations. Next, we introduce Credit System architecture and QoS user portal as part of the JRA2 Support In Science Gateway (task JRA2.3). Finally, we conclude and provide references to JRA2 production.Ce document fournit le premier livrable pour la tâche JRA2 du projet européen European Desktop Grid Initiative (FP7 EDGI). Il est produit par les équipes de l'INRIA, de SZTAKI, du LAL/IN2P3 et de l'Université de Coimbra. Ce document vise à décrire les réalisations et les résultats qui concernent la qualité de service pour l'infrastructure de grilles de PCs européenne EDGI

    Hybrid Distributed Computing Infrastructure Experiments in Grid5000 : Supporting QoS in Desktop Grids with Cloud Resources

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    International audienceHybrid Distributed Computing Infrastructures (DCIs) allow users to combine Grids, Desktop Grids, Clouds, etc. to obtain for their users even larger computing capabilities. In this paper, we present an experimental study of the SpeQuloS framework which aims at providing QoS to Desktop Grid by provisioning on-demand Cloud resources. We describe the experimental platform which relies on Grid5000 to mimic both a Desktop Grid system and a Cloud system. Preliminary results are presented which shows the potential of the SpeQuloS approach

    Extending the EGEE grid with XtremWeb-HEP desktop grids

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    Desktop Grids and Service Grids are widely used by scientific communities to execute high throughput application. The European EDGeS project aims at developing the technologies to bridge these two kinds of Grid technologies together. In this paper, we present the development and the application of these new technology to extend the EGEE Grid with XtremWeb- HEP based Desktop Grids. We present the setup of the distributed infrastructure which enables EGEE users’ jobs to run on one of the three various XtremWeb-HEP DGs. We describe the new volunteer computing project EGEE@Home based on XtremWeb- HEP middleware. To evaluate the capacity of our technology, we present a performance measurement of the main components and conclude that the overhead is kept reasonable despite the fact that the infrastructure is highly distributed
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