Le cloud computing est l’accomplissement du rêve de nombreux informaticiens désireux de transformer et d’utiliser leurs logiciels comme de simples services, rendant ces derniers plus attractifs et séduisants pour les utilisateurs. Dans le cadre de cette thèse, les technologies du cloud computing sont présentées, ainsi que les principaux défis que ce dernier va rencontrer dans un futur proche, notamment pour la gestion et l’analyse des données. A partir de la théorie moderne d'ordonnancements des tâches, nous avons proposé une gestion hiérarchique d’ordonnancements des tâches qui satisfait aux différentes demandes des cloud services. D’un point de vue théorique, nous avons principalement répondu à trois questions cruciales de recherche. Premièrement, nous avons résolu le problème de l'allocation des ressources au niveau de l’utilisateur. Nous avons en particulier proposé des algorithmes basés sur la théorie des jeux. Avec une méthode Bayésienne d’apprentissage, l'allocation des ressources atteint l'équilibre de Nash parmi les utilisateurs en compétition malgré une connaissance insuffisante des comportements de ces derniers. Deuxièmement, nous avons abordé le problème d'ordonnancements des tâches au niveau du système. Nous avons trouvé un nouveau seuil pour l'utilisation d’ordonnancements des tâches en ligne, considérant le dispositif séquentiel de MapReduce. Ce seuil donne de meilleurs résultats que les méthodes existantes dans l’industrie. Troisièmement, nous avons défini un critère de comparaison pour les tests d’ordonnancements de tâches en ligne. Nous avons proposé un concept de fiabilité d'essai pour évaluer la probabilité qu'un ensemble de tâches aléatoires passe un essai donné. Plus la probabilité est grande, plus la fiabilité est élevée. Un test présentant une grande fiabilité garantit une bonne utilisation du système. D’un point de vue pratique, nous avons développé un simulateur basé sur le concept de MapReduce. Ce simulateur offre un environnement directement utilisable par les chercheurs familiers avec SimMapReduce, leur permettant de s’affranchir des aspects informatiques d’implémentations et leur permettant notamment de se concentrer sur les aspects algorithmiques d’un point de vue théorique. / Cloud computing, the long-held dream of computing as a utility, has the potential to transform a large part of the IT industry, making software even more attractive as a service and shaping the way in which hardware is designed and purchased. In this thesis, we reviewed the new cloud computing technologies, and indicated the main challenges for their development in future, among which resource management problem stands out and attracts our attention. Combining the current scheduling theories, we proposed cloud scheduling hierarchy to deal with different requirements of cloud services. From the theoretical aspects, we have accomplished three main research issues. Firstly, we solved the resource allocation problem in the user-level of cloud scheduling. We proposed game theoretical algorithms for user bidding and auctioneer pricing. With Bayesian learning prediction, resource allocation can reach Nash equilibrium among non-cooperative users even though common knowledge is insufficient. Secondly, we addressed the task scheduling problem in the system-level of cloud scheduling. We proved a new utilization bound for on-line schedulability test, considering the sequential feature of MapReduce. We deduced the relationship between cluster utilization bound and the ratio of Map to Reduce. This new schedulable bound with segmentation uplifts classic bound which is most used in industry. Thirdly, we settled the comparison problem among on-line schedulability tests in cloud computing. We proposed a concept of test reliability to evaluate the probability that a random task set could pass a given schedulability test. The larger the probability is, the more reliable the test is. From the aspect of system, a test with high reliability can guarantee high system utilization. From the practical aspects, we have developed a simulator to model MapReduce framework. This simulator offers a simulated environment directly used by MapReduce theoretical researchers. The users of SimMapReduce only concentrate on specific research issues without getting concerned about finer implementation details for diverse service models, so that they can accelerate study progress of new cloud technologies.

Cloud computing, the long-held dream of computing as a utility, has the potential to transform a large part of the IT industry, making software even more attractive as a service and shaping the way in which hardware is designed and purchased. In this thesis, we reviewed the new cloud computing technologies, and indicated the main challenges for their development in future, among which resource management problem stands out and attracts our attention. Combining the current scheduling theories, we proposed cloud scheduling hierarchy to deal with different requirements of cloud services. From the theoretical aspects, we have accomplished three main research issues. Firstly, we solved the resource allocation problem in the user-level of cloud scheduling. We proposed game theoretical algorithms for user bidding and auctioneer pricing. With Bayesian learning prediction, resource allocation can reach Nash equilibrium among non-cooperative users even though common knowledge is insufficient. Secondly, we addressed the task scheduling problem in the system-level of cloud scheduling. We proved a new utilization bound for on-line schedulability test, considering the sequential feature of MapReduce. We deduced the relationship between cluster utilization bound and the ratio of Map to Reduce. This new schedulable bound with segmentation uplifts classic bound which is most used in industry. Thirdly, we settled the comparison problem among on-line schedulability tests in cloud computing. We proposed a concept of test reliability to evaluate the probability that a random task set could pass a given schedulability test. The larger the probability is, the more reliable the test is. From the aspect of system, a test with high reliability can guarantee high system utilization. From the practical aspects, we have developed a simulator to model MapReduce framework. This simulator offers a simulated environment directly used by MapReduce theoretical researchers. The users of SimMapReduce only concentrate on specific research issues without getting concerned about finer implementation details for diverse service models, so that they can accelerate study progress of new cloud technologies.