Fifth Generation (5G) cellular networks will experience a thousand-fold increase in data traffic with over 100 billion connected devices by 2020. In order to support this skyrocketing traffic demand, smaller base stations (BSs) are deployed to increase capacity. However, more BSs increase energy consumption which contributes to operational expenditure (OPEX) and CO2 emissions. Also, an introduction of a plethora of 5G applications running in the mobile devices cause a significant amount of energy consumption in the mobile devices. This thesis presents a novel framework for energy efficiency in 5G cloud radio access networks (C-RAN) by leveraging cloud computing technology. Energy efficiency is achieved in three ways; (i) at the radio side of H-C-RAN (Heterogeneous C-RAN), a dynamic BS switching off algorithm is proposed to minimise energy consumption while maintaining Quality of Service (QoS), (ii) in the BS cloud, baseband workload consolidation schemes are proposed based on simulated annealing and genetic algorithms to minimise energy consumption in the cloud, where also advanced fuzzy based admission control with pre-emption is implemented to improve QoS and resource utilisation (iii) at the mobile device side, Mobile Edge Computing (MEC) is used where computer intensive tasks from the mobile device are executed in the MEC server in the cloud. The simulation results show that the proposed framework effectively reduced energy consumption by up to 48% within RAN and 57% in the mobile devices, and improved network energy efficiency by a factor of 10, network throughput by a factor of 2.7 and resource utilisation by 54% while maintaining QoS.

Fifth Generation (5G) cellular networks will experience a thousand-fold increase in data traffic with over 100 billion connected devices by 2020. In order to support this skyrocketing traffic demand, smaller base stations (BSs) are deployed to increase capacity. However, more BSs increase energy consumption which contributes to operational expenditure (OPEX) and CO2 emissions. Also, an introduction of a plethora of 5G applications running in the mobile devices cause a significant amount of energy consumption in the mobile devices. This thesis presents a novel framework for energy efficiency in 5G cloud radio access networks (C-RAN) by leveraging cloud computing technology. Energy efficiency is achieved in three ways; (i) at the radio side of H-C-RAN (Heterogeneous C-RAN), a dynamic BS switching off algorithm is proposed to minimise energy consumption while maintaining Quality of Service (QoS), (ii) in the BS cloud, baseband workload consolidation schemes are proposed based on simulated annealing and genetic algorithms to minimise energy consumption in the cloud, where also advanced fuzzy based admission control with pre-emption is implemented to improve QoS and resource utilisation (iii) at the mobile device side, Mobile Edge Computing (MEC) is used where computer intensive tasks from the mobile device are executed in the MEC server in the cloud. The simulation results show that the proposed framework effectively reduced energy consumption by up to 48% within RAN and 57% in the mobile devices, and improved network energy efficiency by a factor of 10, network throughput by a factor of 2.7 and resource utilisation by 54% while maintaining QoS.

Fifth Generation (5G) cellular networks will experience a thousand-fold increase in data traffic with over 100 billion connected devices by 2020. In order to support this skyrocketing traffic demand, smaller base stations (BSs) are deployed to increase capacity. However, more BSs increases energy consumption which contributes to operational expenditure (OPEX) and CO2 emissions. Also, an introduction of a plethora of 5G applications running in the mobile devices causes a significant amount of energy consumption in the mobile devices. This thesis presents a novel framework for energy efficiency in 5G cloud radio access networks (C-RAN) by leveraging cloud computing technology. Energy efficiency is achieved in three ways; (i) at the radio side of H-C-RAN (Heterogeneous C-RAN), a dynamic BS switching off algorithm is proposed to minimise energy consumption while maintaining Quality of Service (QoS), (ii) in the BS cloud, baseband workload consolidation schemes are proposed based on simulated annealing and genetic algorithms to minimise energy consumption in the cloud, where also advanced fuzzy based admission control with pre-emption is implemented to improve QoS and resource utilisation (iii) at the mobile device side, Mobile Edge Computing (MEC) is used where computer intensive tasks from the mobile device are executed in the MEC server in the cloud. The simulation results show that the proposed framework effectively reduced energy consumption by up to 48% within RAN and 57% in the mobile devices, and improved network energy efficiency by a factor of 10, network throughput by a factor of 2.7 and resource utilisation by 54% while maintaining QoS.

Cette thèse se place dans le contexte de l’hébergement de services informatiques virtualisés et apporte deux contributions. Elle propose premièrement un système d’aide à la gestion modulaire, déplaçant les machines virtuelles du centre pour le maintenir dans un état satisfaisant. Ce système permet en particulier d’intégrer la notion de consommation électrique des serveurs ainsi que des règles propres à cette consommation. Sa modularité permet de plus l’adaptation de ses composants à des problèmes de grande taille. Cette thèse propose de plus un outil pour comparer différents gestionnaires de centres virtualisés. Cet outil injecte un scénario de montée en charge reproductible dans une infrastructure virtualisée. L’injection d’un tel scénario permet d’évaluer les performances du système de gestion du centre grâce à des sondes spécifiques. Le langage utilisé pour cette injection est extensible et permet l’utilisation de scénarios paramétrés. / This thesis considers the virtualized IT services hosting and makes two contributions. It first proposes a modular system of management aids, to move the virtual machines of the center in order to keep it in a good condition. This system allows in particular to integrate the concept of server power consumption and rules specific to that concept. What’s more, its modularity allows to adjust its components to handle larger problems. This thesis proposes also a tool to compare different virtualized centers managers. This tool injects a reproductible load increase scenario in a virtualized infrastructure. The injection of such a scenario is used to evaluate the performance of the system center manager, using performances probes. The language used for this injection is extensible and allows the creation of parameterized scenarios. The contributions of this thesis were presented in two international conferences and a french conference.

L’un des principaux défis des fournisseurs de services cloud est d’offrir aux utilisateurs une performance acceptable, tout en minimisant les besoins en matériel et énergie. Dans cette thèse CIFRE menée avec Outscale, un fournisseur de cloud, nous visons à optimiser l’allocation des ressources en utilisant de nouvelles sources d’information. Nous caractérisons la charge de travail pour comprendre le stress résultant sur l’orchestrateur, et la compétition pour les ressources disponibles qui dégrade la qualité de service. Nous proposons un modèle pour prédire la durée d’exécution des VMs à partir de caractéristiques prédictives disponibles au démarrage. Enfin, nous évaluons la sensibilité aux erreurs d’un algorithme de placement des VMs de la littérature qui se base sur ces prédictions. Nous ne trouvons pas d’intérêt à coupler note système prédictif avec cet algorithme, mais nous proposons d’autres façons d’utiliser les prédictions pour optimiser le placement des VMs. / One of the main challenges for cloud computing providers remains to offer trustable performance for all users, while maintaining an efficient use of hardware and energy resources. In the context of this CIFRE thesis lead with Outscale, apublic cloud provider, we perform an in-depth study aimed at making management algorithms use new sources of information. We characterize Outscale’s workload to understand the resulting stress for the orchestrator, and the contention for hardware resources. We propose models to predict the runtime of VMs based on features which are available when they start. We evaluate the sensitivity with respect to prediction error of a VM placement algorithm from the literature that requires such predictions. We do not find any advantage in coupling our prediction model and the selected algorithm, but we propose alternative ways to use predictions to optimize the placement of VMs.