A democratic Grid: collaboration, sharing and computing for everyone

This paper presents an integrated vision, architecture, middleware and applications of a public and large scale Grid for everyone: supporting collaboration for groups of people who can interact and share work and share or trade computing resources among them. This public Grid is decentralized and self-adapting to the dynamics of the online world with networks, computers and people who come and go, fail and recover and applications with varying loads and resource needs. Initial evaluation based on the first release of the middleware components and applications shows how our Grid can operate in a dynamic and decentralized environment by a combination of pooling and market mechanisms that adapt supply and demand for resources, self-managing services and applications that react to environmental changes and generic data-sharing services for concurrent write-sharing. The potential for societal impact is enormous as it can open Grid computing and collaboration to everyone on the Internet.Peer ReviewedPostprint (author’s final draft

RDF: A Reconfigurable Dataflow Model of Computation

International audienceDataflow Models of Computation (MoCs) are widely used in embedded systems, including multimedia processing, digital signal processing, telecommunications, and automatic control. In a dataflow MoC, an application is specified as a graph of actors connected by FIFO channels. One of the first and most popular dataflow MoCs, Synchronous Dataflow (SDF), provides static analyses to guarantee boundedness and liveness, which are key properties for embedded systems. However, SDF and most of its variants lack the capability to express the dynamism needed by modern streaming applications. In particular, the applications mentioned above have a strong need for reconfigurability to accommodate changes in the input data, the control objectives, or the environment. We address this need by proposing a new MoC called Reconfigurable Dataflow (RDF). RDF extends SDF with transformation rules that specify how and when the topology and actors of the graph may be reconfigured. Starting from an initial RDF graph and a set of transformation rules, an arbitrary number of new RDF graphs can be generated at runtime. A key feature of RDF is that it can be statically analyzed to guarantee that all possible graphs generated at runtime will be consistent and live. We introduce the RDF MoC, describe its associated static analyses, and present its implementation and some experimental results

RDF: Un modèle de calcul flot de données reconfigurable

Dataflow Models of Computation (MoCs) are widely used in embedded systems, including multimedia processing, digital signal processing, telecommunications, and automatic control. In a dataflow MoC, an application is specified as a graph of actors connected by FIFO channels. One of the first and most popular dataflow MoCs, Synchronous Dataflow (SDF), provides static analyses to guarantee boundedness and liveness, which are key properties for embedded systems. However, SDF and most of its variants lacks the capability to express the dynamism needed by modern streaming applications. In particular, the applications mentioned above have a strong need for reconfigurability to accommodate changes in the input data, the control objectives, or the environment. We address this need by proposing a new MoC called Reconfigurable Dataflow (RDF). RDF extends SDF with transformation rules that specify how and when the topology and actors of the graph may be reconfigured. Starting from an initial RDF graph and a set of transformation rules, an arbitrary number of new RDF graphs can be generated at runtime. A key feature of RDF is that it can be statically analyzed to guarantee that all possible graphs generated at runtime will be consistent and live. We introduce the RDF MoC, describe its associated static analyses, and present its implementation and some experimental results.Les modèles de calcul (MoCs) flot de données synchrones sont très utilisés dans les systèmes embarqués et les applications multimédia, de traitement du signal, de télécommunication et de contrôle automatique. Dans ce style de modèle, une application est spécifiée par un graphe d’acteurs connectés par des liens FIFO de communication. Un des MoCs les plus connus, SDF (pour Synchronous Dataflow), permet des analyses statiques qui garantissent l’exécution en mémoire bornée et l’absence d’interblocage, propriétés clés pour les systèmes embarqués. Néanmoins, SDF (et la plupart de ses variantes) ne permet pas d’exprimer la dynamicité requise par les applications embarquées modernes. En particulier, ces applications ont souvent besoin de se reconfigurer pour s’adapter aux changements (par ex., de débit ou de qualité) du flot d’entrée, des objectifs de contrôle ou de l’environnement. Afin de répondre à ce besoin, nous proposons RDF (pour Reconfigurable DataFlow) un MoC qui étend SDF avec des règles de transformations spécifiant comment la topologie du graphe flot de données peut être reconfiguré dynamiquement. En considérant un graphe SDF initial et un ensemble de règles de transformation, un nombre arbitraire de nouveaux graphes peuvent être produits. La principale qualité de RDF est qu’il peut être analysé statiquement pour garantir que tous les graphes générés dynamiquement s’exécuteront en mémoire bornée et sans interblocage. Nous présentons le modèle RDF, les analyses statiques associées, sa mise en oeuvre et quelques expérimentations

RDF : un modèle flot de données reconfigurable(version étendue)

Dataflow Models of Computation (MoCs) are widely used in embedded systems, including multimedia processing, digital signal processing, telecommunications, and automatic control. In a dataflow MoC, an application is specified as a graph of actors connected by FIFO channels. One of the most popular dataflow MoCs, Synchronous Dataflow (SDF), provides static analyses to guarantee boundedness and liveness, which are key properties for embedded systems. However, SDF (and most of its variants) lacks the capability to express the dynamism needed by modern streaming applications. In particular, the applications mentioned above have a strong need for reconfigurability to accommodate changes in the input data, the control objectives, or the environment.We address this need by proposing a new MoC called Reconfigurable Dataflow (RDF). RDF extends SDF with transformation rules that specify how the topology and actors of the graph may be reconfigured. Starting from an initial RDF graph and a set of transformation rules, an arbitrary number of new RDF graphs can be generated at runtime. A key feature of RDF is that it can be statically analyzed to guarantee that all possible graphs generated at runtime will be consistent and live. We introduce the RDF MoC, describe its associated static analyses, and outline its implementation.Les modèles de calcul (MoCs) flot de données synchrones sont très utilisés dans les systèmes embarqués pour les applications multimédia, de traitement du signal, de télécommunication et de contrôle automatique. Dans ce style de modèle, une application est spécifiée par un graphe d’acteurs connectés par des liens FIFO de communication. Un des MoCs les plus connus, SDF (pour Synchronous Dataflow), permet des analyses statiques qui garantissent l’exécution enmémoire bornée et l’absence d’interblocage, propriétés clés pour les systèmes embarqués. Néanmoins, SDF (et la plupart de ses variantes) ne permet pas d’exprimer la dynamicité requise par les applications embarquées modernes. En particulier, ces applications ont souvent besoin de se reconfigurer pour s’adapter aux changements (par ex., de débit ou de qualité) du flot d’entrée, des objectifs de contrôle ou de l’environnement.Afin de répondre à ce besoin, nous proposons le MoC RDF (pour Reconfigurable DataFlow) qui étend SDF avec des règles de transformations spécifiant comment la topologie et les acteurs du graphe peuvent être reconfigurés dynamiquement. En considérant un graphe SDF initial et un ensemble de règles de transformation, un nombre arbitraire de nouveaux graphes peuvent être produits. La principale qualité de RDF est qu’il peut être analysé statiquement pour garantir que tous les graphes générés dynamiquement s’exécuteront en mémoire bornée et sans interblocage.Nous présentons le modèle RDF, décrivons les analyses statiques associées et décrivons brièvementson implémentation

Anticancer Evaluation of Adiantum venustum Don

Cancer is a malignant disease that is characterized by rapid and uncontrolled formation of abnormal cells which may mass together to form a growth or tumor, or proliferate throughout the body. Next to heart disease, cancer is a major killer of mankind. This study aims at a preliminary phytochemical screening and anticancer evaluation of Adiantum venustum Don against Ehrlich Ascites Carcinoma in animal model. The findings indicate that ethanolic extract of A. venustum Don possesses significant anticancer activity and also reduces elevated level of lipid peroxidation due to the presence of terpenoids and flavonoids. Thus, ethanolic extract of A. venustum Don could have vast therapeutic application against cancer

A democratic Grid: collaboration, sharing and computing for everyone

This paper presents an integrated vision, architecture, middleware and applications of a public and large scale Grid for everyone: supporting collaboration for groups of people who can interact and share work and share or trade computing resources among them. This public Grid is decentralized and self-adapting to the dynamics of the online world with networks, computers and people who come and go, fail and recover and applications with varying loads and resource needs. Initial evaluation based on the first release of the middleware components and applications shows how our Grid can operate in a dynamic and decentralized environment by a combination of pooling and market mechanisms that adapt supply and demand for resources, self-managing services and applications that react to environmental changes and generic data-sharing services for concurrent write-sharing. The potential for societal impact is enormous as it can open Grid computing and collaboration to everyone on the Internet.Peer Reviewe

A democratic Grid: collaboration, sharing and computing for everyone

This paper presents an integrated vision, architecture, middleware and applications of a public and large scale Grid for everyone: supporting collaboration for groups of people who can interact and share work and share or trade computing resources among them. This public Grid is decentralized and self-adapting to the dynamics of the online world with networks, computers and people who come and go, fail and recover and applications with varying loads and resource needs. Initial evaluation based on the first release of the middleware components and applications shows how our Grid can operate in a dynamic and decentralized environment by a combination of pooling and market mechanisms that adapt supply and demand for resources, self-managing services and applications that react to environmental changes and generic data-sharing services for concurrent write-sharing. The potential for societal impact is enormous as it can open Grid computing and collaboration to everyone on the Internet.Peer Reviewe

AREN: A Popularity Aware Replication Scheme for Cloud Storage

International audienceDelivering on-demand web content to end-users in order to carry out strict QoS metrics is not a trivial task for globally distributed network providers. This task becomes still harder when content popularity varies over the time and the SLA definitions have to include both transfer rate and latency metrics. Current worldwide content delivery approaches and datacenter infrastructures rely on cumbersome replication schemes that are agnostic to edge-network resources, and damage content provision. In this work we present AREN, an novel replication scheme for cloud storage on edge networks. AREN relies on a collaborative cache strategy and bandwidth reservation to adapt the replication degree according to strict SLA contracts and content popularity growth. We have evaluated the performances of replication schemes on edge networks using Caju, a content distribution system for edge networks. Compared to a non-collaborative caching, evaluations show that AREN prevents nearly 99.8% of all SLA violations when the storage system is heavily loaded. We also show that AREN provides a sevenfold decrease in the amount of storage usage for replicas, and it increases by roughly 20% the aggregate bandwidth, hence accelerating content delivery

RDF : un modèle flot de données reconfigurable(version étendue)

Dataflow Models of Computation (MoCs) are widely used in embedded systems, including multimedia processing, digital signal processing, telecommunications, and automatic control. In a dataflow MoC, an application is specified as a graph of actors connected by FIFO channels. One of the most popular dataflow MoCs, Synchronous Dataflow (SDF), provides static analyses to guarantee boundedness and liveness, which are key properties for embedded systems. However, SDF (and most of its variants) lacks the capability to express the dynamism needed by modern streaming applications. In particular, the applications mentioned above have a strong need for reconfigurability to accommodate changes in the input data, the control objectives, or the environment.We address this need by proposing a new MoC called Reconfigurable Dataflow (RDF). RDF extends SDF with transformation rules that specify how the topology and actors of the graph may be reconfigured. Starting from an initial RDF graph and a set of transformation rules, an arbitrary number of new RDF graphs can be generated at runtime. A key feature of RDF is that it can be statically analyzed to guarantee that all possible graphs generated at runtime will be consistent and live. We introduce the RDF MoC, describe its associated static analyses, and outline its implementation.Les modèles de calcul (MoCs) flot de données synchrones sont très utilisés dans les systèmes embarqués pour les applications multimédia, de traitement du signal, de télécommunication et de contrôle automatique. Dans ce style de modèle, une application est spécifiée par un graphe d’acteurs connectés par des liens FIFO de communication. Un des MoCs les plus connus, SDF (pour Synchronous Dataflow), permet des analyses statiques qui garantissent l’exécution enmémoire bornée et l’absence d’interblocage, propriétés clés pour les systèmes embarqués. Néanmoins, SDF (et la plupart de ses variantes) ne permet pas d’exprimer la dynamicité requise par les applications embarquées modernes. En particulier, ces applications ont souvent besoin de se reconfigurer pour s’adapter aux changements (par ex., de débit ou de qualité) du flot d’entrée, des objectifs de contrôle ou de l’environnement.Afin de répondre à ce besoin, nous proposons le MoC RDF (pour Reconfigurable DataFlow) qui étend SDF avec des règles de transformations spécifiant comment la topologie et les acteurs du graphe peuvent être reconfigurés dynamiquement. En considérant un graphe SDF initial et un ensemble de règles de transformation, un nombre arbitraire de nouveaux graphes peuvent être produits. La principale qualité de RDF est qu’il peut être analysé statiquement pour garantir que tous les graphes générés dynamiquement s’exécuteront en mémoire bornée et sans interblocage.Nous présentons le modèle RDF, décrivons les analyses statiques associées et décrivons brièvementson implémentation