A stochastic approach for optimizing green energy consumption in distributed clouds

Benjamin Camus 1 Fanny Dufossé 2 Anne-Cécile Orgerie 3, 1
1 MYRIADS - Design and Implementation of Autonomous Distributed Systems
Inria Rennes – Bretagne Atlantique , IRISA-D1 - SYSTÈMES LARGE ÉCHELLE
2 DOLPHIN - Parallel Cooperative Multi-criteria Optimization
Inria Lille - Nord Europe, CRIStAL - Centre de Recherche en Informatique, Signal et Automatique de Lille (CRIStAL) - UMR 9189
Abstract : The energy drawn by Cloud data centers is reaching worrying levels, thus inciting providers to install on-site green energy producers, such as photovoltaic panels. Considering distributed Clouds, workload managers need to geographically allocate virtual machines according to the green production in order not to waste energy. In this paper, we propose SAGITTA: a Stochastic Approach for Green consumption In disTributed daTA centers. We show that compared to the optimal solution, SAGITTA consumes 4% more brown energy, and wastes only 3.14% of the available green energy, while a traditional round-robin solution consumes 14.4% more energy overall than optimum, and wastes 28.83% of the available green energy.
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Benjamin Camus, Fanny Dufossé, Anne-Cécile Orgerie. A stochastic approach for optimizing green energy consumption in distributed clouds. SMARTGREENS 2017 - International Conference on Smart Cities and Green ICT Systems, Apr 2017, Porto, Portugal. ⟨hal-01475431⟩

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