A Logistic Mobile Application based on Internet of Things

Abstract-A Logistic Mobile Application is presented. The application is based on Internet of Things and combines a communication infrastructure and a High Performance Computing infrastructure in order to deliver mobile logistic services with high quality of service and adaptation to the dynamic nature of logistic operations

Un environnement pour le calcul intensif pair à pair

Le concept de pair à pair (P2P) a connu récemment de grands développements dans les domaines du partage de fichiers, du streaming vidéo et des bases de données distribuées. Le développement du concept de parallélisme dans les architectures de microprocesseurs et les avancées en matière de réseaux à haut débit permettent d'envisager de nouvelles applications telles que le calcul intensif distribué. Cependant, la mise en oeuvre de ce nouveau type d'application sur des réseaux P2P pose de nombreux défis comme l'hétérogénéité des machines, le passage à l'échelle et la robustesse. Par ailleurs, les protocoles de transport existants comme TCP et UDP ne sont pas bien adaptés à ce nouveau type d'application. Ce mémoire de thèse a pour objectif de présenter un environnement décentralisé pour la mise en oeuvre de calculs intensifs sur des réseaux pair à pair. Nous nous intéressons à des applications dans les domaines de la simulation numérique et de l'optimisation qui font appel à des modèles de type parallélisme de tâches et qui sont résolues au moyen d'algorithmes itératifs distribués or parallèles. Contrairement aux solutions existantes, notre environnement permet des communications directes et fréquentes entre les pairs. L'environnement est conçu à partir d'un protocole de communication auto-adaptatif qui peut se reconfigurer en adoptant le mode de communication le plus approprié entre les pairs en fonction de choix algorithmiques relevant de la couche application ou d'éléments de contexte comme la topologie au niveau de la couche réseau. Nous présentons et analysons des résultats expérimentaux obtenus sur diverses plateformes comme GRID'5000 et PlanetLab pour le problème de l'obstacle et des problèmes non linéaires de flots dans les réseaux. ABSTRACT : The concept of peer-to-peer (P2P) has known great developments these years in the domains of file sharing, video streaming or distributed databases. Recent advances in microprocessors architecture and networks permit one to consider new applications like distributed high performance computing. However, the implementation of this new type of application on P2P networks gives raise to numerous challenges like heterogeneity, scalability and robustness. In addition, existing transport protocols like TCP and UDP are not well suited to this new type of application. This thesis aims at designing a decentralized and robust environment for the implementation of high performance computing applications on peer-to-peer networks. We are interested in applications in the domains of numerical simulation and optimization that rely on tasks parallel models and that are solved via parallel or distributed iterative algorithms. Unlike existing solutions, our environment allows frequent direct communications between peers. The environment is based on a self adaptive communication protocol that can reconfigure itself dynamically by choosing the most appropriate communication mode between any peers according to decisions concerning algorithmic choice made at the application level or elements of context at transport level, like topology. We present and analyze computational results obtained on several testeds like GRID’5000 and PlanetLab for the obstacle problem and nonlinear network flow problems

ADEPT Runtime/Scalability Predictor in support of Adaptive Scheduling

A job scheduler determines the order and duration of the allocation of resources, e.g. CPU, to the tasks waiting to run on a computer. Round-Robin and First-Come-First-Serve are examples of algorithms for making such resource allocation decisions. Parallel job schedulers make resource allocation decisions for applications that need multiple CPU cores, on computers consisting of many CPU cores connected by different interconnects. An adaptive parallel scheduler is a parallel scheduler that is capable of adjusting its resource allocation decisions based on the current resource usage and demand. Adaptive parallel schedulers that decide the numbers of CPU cores to allocate to a parallel job provide more flexibility and potentially improve performance significantly for both local and grid job scheduling compared to non-adaptive schedulers. A major reason why adaptive schedulers are not yet used practically is due to lack of knowledge of the scalability curves of the applications, and high cost of existing white-box approaches for scalability prediction. We show that a runtime and scalability prediction tool can be developed with 3 requirements: accuracy comparable to white-box methods, applicability, and robustness. Applicability depends only on knowledge feasible to gain in a production environment. Robustness addresses anomalous behaviour and unreliable predictions. We present ADEPT, a speedup and runtime prediction tool that satisfies all criteria for both single problem size and across different problem sizes of a parallel application. ADEPT is also capable of handling anomalies and judging reliability of its predictions. We demonstrate these using experiments with MPI and OpenMP implementations of NAS benchmarks and seven real applications

Performance Prediction in a Decentralized Environment for Peer-to-Peer Computing

International audienceP2PDC is an environment for high performance peer to peer computing that allows direct communication between peers. This environment is based on P2PSAP, a self adaptive communication protocol. P2PDC is suited to the solution of large scale numerical simulation problems via distributed iterative methods. dPerf is a performance prediction environment for parallel and distributed applications, with primary interest in programs written in C, C++, Fortran for P2PDC. The dPerf performance prediction tool makes use of static and dynamic analyses combined with trace-based simulation. In this paper, we present a decentralized version of P2PDC and show how dPerf predicts performance for the P2PDC environment. We present new features of P2PDC aimed at making it more scalable and robust. Through experiments with P2PDC and dPerf, we show how to properly choose a peer to peer computing system which can match the computing power of a cluster