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Felber, Pascal

Nom
Felber, Pascal
Affiliation principale
Site web
Fonction
Professeur ordinaire
Email
pascal.felber@unine.ch
Identifiants
https://libra.unine.ch/handle/123456789/283
_
0000-0003-1574-6721

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  • Publication
    Accès libre
    Multi-Level Energy Effciency for Heterogeneous Data Centers
    (Neuchâtel : Université de Neuchâtel, 2017)
    Kurpicz-Briki, Masha
    ;
    Felber, Pascal 
    The ICT sector has an important impact on global energy consumption (building devices, building networks, operation, air-conditioning and more). Studies show that cloud computing as a whole consumes more energy than entire countries like Germany or India. Furthermore, recent estimates have shown that the cloud computing sector, and thus the energy consumed by data centers, is still increasing. The major goal of this thesis is to increase the energy awareness in heterogeneous data centers and thus contribute to the reduction of energy consumption in the ICT sector. To improve the energy awareness, we need to know how much energy is spent. This is in uenced not only by the infrastructure, but by every single application running in the data center. Unfortunately we cannot measure application energy consumption with physical power meters. Therefore, this thesis contains real-world models to estimate the energy consumption at di erent levels. This work is organized as a stack with 3 layers: (1) data center, (2) host/virtualization, (3) end user applications. At the data center layer, we first study the impact of different workloads running on heterogeneous machines. In a second step, we develop EPAVE, a model for energy-proportional accounting in VM-based environments. EPAVE is supported by PowerIndex, a profiling and energy estimation framework. At the host/virtualization layer, we present BitWatts, a middleware toolkit for building software-defined power meters. With BitWatts we cross the boundaries of virtual environments and provide an estimation of the power consumption of applications running within virtual machines. At the bottom of the stack we look into battery modelling to extend the battery life of mobile devices.