[pt] Novos contextos da computação distribuída reforçam a necessidade de abstra ções de programação que favoreçam a comunicação assíncrona e o tratamento de tarefas concorrentes. Modelos de programação dirigido a eventos ou baseado em threads incorporam decisões sobre como tratar essas questões, em cada caso apresentando vantagens e desvantagens. Normalmente, a opção pelo modelo mais adequado para um determinado contexto ou problema exige que o programador lide diretamente com as desvantagens inerentes a esse modelo, tornando a tarefa de desenvolvimento de aplicações mais complexa. Nesse trabalho aprofundamos a discussão sobre como combinar as vantagens dos modelos tradicionais de programação para oferecer uma interface de programação mais simples para o programador. Exploramos o uso de operações de comunicação não- bloqueante e de um mecanismo básico de gerência cooperativa de tarefas baseado na construção de co-rotinas. Usando esses conceitos implementamos: (1) um conjunto de operações que facilitam o desenvolvimento de aplicações com a estrutura cliente/servidor sobre uma base de comunicação assíncrona; e (2) uma interface de programação mais apropriada para o TinyOS, o estado da arte em sistema operacional para redes de sensores. / [en] New contexts of distributed computing emphasize the need of programming abstractions able to deal with asynchronous communication and concurrent tasks. Event-driven or threaded programming models are able to deal with these issues, but each model presents particular advantages and problems. Normally, when choosing the model more appropriate for a context or problem, the programmer must deal directly with the difficulties related to this model, making the development task more complex. In this work we discuss a way to combine the advantages of the traditional programming models in order to support a programming interface more suitable for the programmer.We explore asynchronous communication and cooperative task management based on the co-routine construction. By using these concepts, we implement: (1) a set of operations for building client/server applications upon an asynchronous communication basis; and (2) a more appropriate programming interface for TinyOS, the state of the art of operating system for sensor networks.

Cette thèse couvre le sujet des systèmes de traitement de données àgrande échelle, et plus précisément trois approches complémentaires :la conception d'un système pour prédir des défaillances de serveursgrâce à l'analyse de leurs données de supervision; l'acheminement dedonnées dans un système à temps réel en étudiant les corrélationsentre les champs des messages pour favoriser la localité; etfinalement un environnement de développement innovateur pour concevoirdes transformations de donées en utilisant des graphes orientés deblocs.À travers le projet Smart Support Center, nous concevons unearchitecture qui passe à l'échelle, afin de stocker des sériestemporelles rapportées par des moteurs de supervision, qui vérifienten permanence la santé des systèmes informatiques. Nous utilisons cesdonnées pour effectuer des prédictions, et détecter de potentielsproblèmes avant qu'ils ne ne produisent.Nous nous plongeons ensuite dans les algorithmes d'acheminement pourles sytèmes de traitement de données en temps réel, et développons unecouche pour acheminer les messages plus efficacement, en évitant lesrebonds entre machines. Dans ce but, nous identifions en temps réelles corrélations qui apparaissent entre les champs de ces messages,tels les mots-clics et leur localisation géographique, par exempledans le cas de micromessages. Nous utilisons ces corrélations pourcréer des tables d'acheminement qui favorisent la colocation desacteurs traitant ces messages.Pour finir, nous présentons λ-blocks, un environnement dedéveloppement pour effectuer des tâches de transformations de donnéessans écrire de code source, mais en créant des graphes de blocs decode. L'environnement est rapide, et est distribué avec des pilesincluses: libraries de blocs, modules d'extension, et interfaces deprogrammation pour l'étendre. Il est également capable de manipulerdes graphes d'exécution, pour optimisation, analyse, vérification, outout autre but. / This thesis covers the topic of large-scale data processing systems,and more precisely three complementary approaches: the design of asystem to perform prediction about computer failures through theanalysis of monitoring data; the routing of data in a real-time systemlooking at correlations between message fields to favor locality; andfinally a novel framework to design data transformations usingdirected graphs of blocks.Through the lenses of the Smart Support Center project, we design ascalable architecture, to store time series reported by monitoringengines, which constantly check the health of computer systems. We usethis data to perform predictions, and detect potential problems beforethey arise.We then dive in routing algorithms for stream processing systems, anddevelop a layer to route messages more efficiently, by avoiding hopsbetween machines. For that purpose, we identify in real-time thecorrelations which appear in the fields of these messages, such ashashtags and their geolocation, for example in the case of tweets. Weuse these correlations to create routing tables which favor theco-location of actors handling these messages.Finally, we present λ-blocks, a novel programming framework to computedata processing jobs without writing code, but rather by creatinggraphs of blocks of code. The framework is fast, and comes withbatteries included: block libraries, plugins, and APIs to extendit. It is also able to manipulate computation graphs, foroptimization, analyzis, verification, or any other purposes.