La consommation énergétique des centres de calculs et de données, aussi appelés « data centers », représentait 2% de la consommation mondiale d'électricité en 2012. Leur nombre est en augmentation et suit l'évolution croissante des objets connectés, services, applications, et des données collectées. Ces infrastructures, très consommatrices en énergie, sont souvent sur-dimensionnées et les serveurs en permanence allumés. Quand la charge de travail est faible, l'électricité consommée par les serveurs inutilisés est gaspillée, et un serveur inactif peut consommer jusqu'à la moitié de sa consommation maximale. Cette thèse s'attaque à ce problème en concevant un data center ayant une consommation énergétique proportionnelle à sa charge. Nous proposons un data center hétérogène, nommé BML pour « Big, Medium, Little », composé de plusieurs types de machines : des processeurs très basse consommation et des serveurs classiques. L'idée est de profiter de leurs différentes caractéristiques de performance, consommation, et réactivité d'allumage, pour adapter dynamiquement la composition de l'infrastructure aux évolutions de charge. Nous décrivons une méthode générique pour calculer les combinaisons de machines les plus énergétiquement efficaces à partir de données de profilage de performance et d'énergie acquis expérimentalement considérant une application cible, ayant une charge variable au cours du temps, dans notre cas un serveur web.Nous avons développé deux algorithmes prenant des décisions de reconfiguration de l'infrastructure et de placement des instances de l'application en fonction de la charge future. Les différentes temporalités des actions de reconfiguration ainsi que leur coûts énergétiques sont pris en compte dans le processus de décision. Nous montrons par simulations que nous atteignons une consommation proportionnelle à la charge, et faisons d'importantes économies d'énergie par rapport aux gestions classiques des data centers. / The increasing number of data centers raises serious concerns regarding their energy consumption. These infrastructures are often over-provisioned and contain servers that are not fully utilized. The problem is that inactive servers can consume as high as 50% of their peak power consumption.This thesis proposes a novel approach for building data centers so that their energy consumption is proportional to the actual load. We propose an original infrastructure named BML for "Big, Medium, Little", composed of heterogeneous computing resources : from low power processors to classical servers. The idea is to take advantage of their different characteristics in terms of energy consumption, performance, and switch on reactivity to adjust the composition of the infrastructure according to the load evolutions. We define a generic methodology to compute the most energy proportional combinations of machines based on hardware profiling data.We focus on web applications whose load varies over time and design a scheduler that dynamically reconfigures the infrastructure, with application migrations and machines switch on and off, to minimize the infrastructure energy consumption according to the current application requirements.We have developed two different dynamic provisioning algorithms which take into account the time and energy overheads of the different reconfiguration actions in the decision process. We demonstrate through simulations based on experimentally acquired hardware profiles that we achieve important energy savings compared to classical data center infrastructures and management.

Buildings are directly responsible for 40% of the final energy use in most developed economies and for much more if indirect requirements are considered. This results in huge impacts which affect the environmental balance of our planet.<p>However, most current building energy assessments focus solely on operational energy overlooking other energy uses such as embodied and transport energy. Embodied energy comprises the energy requirements for building materials production, construction and replacement. Transport energy represents the amount of energy required for the mobility of building users.<p>Decisions based on partial assessments might result in an increased energy demand during other life cycle stages or at different scales of the built environment. Recent studies have shown that embodied and transport energy demands often account for more than half of the total lifecycle energy demand of residential buildings. Current assessment tools and policies therefore overlook more than 50% of the life cycle energy use.<p>This thesis presents a comprehensive life cycle energy analysis framework for residential buildings. This framework takes into account energy requirements at the building scale, i.e. the embodied and operational energy demands, and at the city scale, i.e. the embodied energy of nearby infrastructures and the transport energy of its users. This framework is implemented through the development, verification and validation of an advanced software tool which allows the rapid analysis of the life cycle energy demand of residential buildings and districts. Two case studies, located in Brussels, Belgium and Melbourne, Australia, are used to investigate the potential of the developed framework.<p>Results show that each of the embodied, operational and transport energy requirements represent a significant share of the total energy requirements and associated greenhouse gas emissions of a residential building, over its useful life. The use of the developed tool will allow building designers, town planners and policy makers to reduce the energy demand and greenhouse gas emissions of residential buildings by selecting measures that result in overall savings. This will ultimately contribute to reducing the environmental impact of the built environment. / Doctorat en Sciences de l'ingénieur / info:eu-repo/semantics/nonPublished