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.
Liste complète des métadonnées

https://hal.archives-ouvertes.fr/hal-01945821
Contributor : Anne-Cécile Orgerie <>
Submitted on : Wednesday, December 5, 2018 - 3:28:28 PM
Last modification on : Thursday, February 7, 2019 - 4:10:53 PM
Document(s) archivé(s) le : Wednesday, March 6, 2019 - 2:36:25 PM

File

chapter.pdf
Files produced by the author(s)

Identifiers

  • HAL Id : hal-01945821, version 1

Citation

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⟩

Share

Metrics

Record views

251

Files downloads

50