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Scheduling on Clouds considering energy consumption and performance trade-offs : from modelization to industrial applications

Abstract : Modern society relies heavily on the use of computational resources. Over the last decades, the number of connected users and deviees has dramatically increased, leading to the consideration of decentralized on-demand computing as a utility, commonly named "The Cloud". Numerous fields of application such as High Performance Computing (HPC). medical research, movie rendering , industrial facto ry processes or smart city management , benefit from recent advances of on-demand computation .The maturity of Cloud technologies led to a democratization and to an explosion of connected services for companies, researchers, techies and even mere mortals, using those resources in a pay-per-use fashion.ln particular, since the Cloud Computing paradigm has since been adopted in companies . A significant reason is that the hardware running the cloud andprocessing the data does not reside at a company physical site, which means thatthe company does not have to build computer rooms (known as CAPEX, CAPitalEXpenditures) or buy equipment, nor to fill and mainta in that equipment over a normal life-cycle (known as OPEX, Operational EXpenditures).This thesis revolves around the energy efficiency of Cloud platforms by proposing an extensible and multi-criteria framework, which intends to improve the efficiency of an heterogeneous platform from an energy consumption perspective. We propose an approach based on user involvement using the notion of a cursor offering the ability to aggregate cloud operator and end user preferences to establish scheduling policies . The objective is the right sizing of active servers and computing equipments while considering exploitation constraints, thus reducing the environmental impactassociated to energy wastage.This research work has been validated on experiments and simulations on the Grid'SOOO platform, the biggest shared network in Europe dedicated to research.lt has been integrated to the DIET middleware, and a industrial valorisation has beendone in the NUVEA commercial platform, designed during this thesis . This platform constitutes an audit and optimization tool of large scale infrastructures for operatorsand end users
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  • HAL Id : tel-01436822, version 2

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Daniel Balouek-Thomert. Scheduling on Clouds considering energy consumption and performance trade-offs : from modelization to industrial applications. Distributed, Parallel, and Cluster Computing [cs.DC]. Université de Lyon, 2016. English. ⟨NNT : 2016LYSEN058⟩. ⟨tel-01436822v2⟩

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