The SAGITTA approach for optimizing solar energy consumption in distributed clouds with stochastic modeling

Benjamin Camus 1 Fanny Dufossé 2 Anne-Cécile Orgerie 1
1 MYRIADS - Design and Implementation of Autonomous Distributed Systems
Inria Rennes – Bretagne Atlantique , IRISA_D1 - SYSTÈMES LARGE ÉCHELLE
2 DATAMOVE - Data Aware Large Scale Computing
Inria Grenoble - Rhône-Alpes, LIG - Laboratoire d'Informatique de Grenoble
Abstract : Facing the urgent need to decrease data centers' energy consumption, Cloud providers resort to on-site renewable energy production. Solar energy can thus be used to power data centers. Yet this energy production is intrinsically uctuating over time and depending on the geographical location. In this paper, we propose a stochastic modeling for optimizing solar energy consumption in distributed clouds. Our approach, named SAGITTA (Stochastic Approach for Green consumption In disTributed daTA centers), is shown to produce a virtual machine scheduling close to the optimal algorithm in terms of energy savings and to outperform classical round-robin approaches over varying Cloud workloads and real solar energy generation traces.
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Chapitre d'ouvrage
Smart Cities, Green Technologies, and Intelligent Transport Systems, pp.1-25, 2018
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https://hal.archives-ouvertes.fr/hal-01945821
Contributeur : Anne-Cécile Orgerie <>
Soumis le : mercredi 5 décembre 2018 - 15:28:28
Dernière modification le : jeudi 7 février 2019 - 16:10:53

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  • HAL Id : hal-01945821, version 1

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Benjamin Camus, Fanny Dufossé, Anne-Cécile Orgerie. The SAGITTA approach for optimizing solar energy consumption in distributed clouds with stochastic modeling. Smart Cities, Green Technologies, and Intelligent Transport Systems, pp.1-25, 2018. 〈hal-01945821〉

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