{'fraction_non_alphanumeric': 0.013616557734204794, 'fraction_numerical': 0.0010893246187363835, 'mean_word_length': 6.011450381679389, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Scheduling and Dynamic Provisioning for Energy Proportional Heterogeneous Infrastructures. (Ordonnancement et Allocation Dynamique de Ressources pour des Infrastructures Hétérogènes à Consommation Energétique Proportionnelle)

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.