Les avancées technologiques ont conduit les grandes organisations telles que les entreprises,les universités et les instituts de recherche à se doter d'intranets constitués de plusieurs serveurs etd'un grand nombre de postes de travail. Cependant dans certaines de ces organisations, les postes detravail sont très peu utilisés pendant la nuit, les week-ends et les périodes de congés, libérant ainsiune grande puissance de calcul disponible et inutilisée.Dans cette thèse, nous étudions l'exploitation de ces temps de jachère afin d'exécuter desapplications de calcul haute performance. A cet effet, nous supposons que les postes acquis sontrebootés et intégrés à des grappes virtuelles constituées dynamiquement. Toutefois, ces temps dejachère ne permettent pas toujours d'exécuter les applications jusqu'à leur terme. Les mécanismes desauvegarde/reprise (checkpointing) sont alors utilisés pour sauvegarder, dans un certain délai, lecontexte d'exécution des applications en vue d'une éventuelle reprise. Il convient de noter que lasauvegarde de tous les processus dans les délais impartis n'est pas toujours possible. Nousproposons un modèle d'ordonnancement des sauvegardes en parallèle, qui tient compte descontraintes temporelles imposées et des contraintes liées aux bandes passantes (réseau et disque),pour maximiser les temps de calcul déjà effectués pour les applications candidates à la sauvegarde. / The technological advances has led major organizations such as enterprises, universities andresearch institutes to acquire intranets consisting of several servers and many workstations.However, in some of these organizations, the resources are rarely used at nights, weekends and onholidays, thus releasing a large computing power available and unused.This thesis discusses the exploitation of the idle period of workstaions in order to run HPCapplications. The workstations retained are restarted and integrated in dynamically formed clusters.However, the idle periods do not always permit the complete carrying out of the computationsallocated to them. The checkpointing mechanisms are then used to save in a certain period, theexecution context of applications for a possible restart. It is worth nothing that checkpointing all theprocesses in the required period is not always possible. We propose a scheduling model ofcheckpointing in parallel, which takes into account the time constraints imposed and the bandwidthconstraints (network and disk) to maximize the computation time already taken for the applicationswhich are to be checkpointed.

According to Sterling et al., a batch scheduler, also called workload management, is an application or set of services that provide a method to monitor and manage the flow of work through the system [Sterling01]. The purpose of this research was to develop a method to assess the execution speed of workloads that are submitted to a batch scheduler. While previous research exists, this research is different in that more complex jobs were devised that fully exercised the scheduler with established benchmarks. This research is important because the reduction of latency even if it is miniscule can lead to massive savings of electricity, time, and money over the long term. This is especially important in the era of green computing [Reuther18]. The methodology used to assess these schedulers involved the execution of custom automation scripts. These custom scripts were developed as part of this research to automatically submit custom jobs to the schedulers, take measurements, and record the results. There were multiple experiments conducted throughout the course of the research. These experiments were designed to apply the methodology and assess the execution speed of a small selection of batch schedulers. Due to time constraints, the research was limited to four schedulers. x The measurements that were taken during the experiments were wall time, RAM usage, and CPU usage. These measurements captured the utilization of system resources of each of the schedulers. The custom scripts were executed using, 1, 2, and 4 servers to determine how well a scheduler scales with network growth. The experiments were conducted on local school resources. All hardware was similar and was co-located within the same data-center. While the schedulers that were investigated as part of the experiments are agnostic to whether the system is grid, cluster, or super-computer; the investigation was limited to a cluster architecture.