Combiner la programmation par contraintes et l’apprentissage machine pour construire un modèle éco-énergétique pour petits et moyens data centers

Abstract : Over the last decade, cloud computing technologies have considerably grown, this translates into a surge in data center power consumption. The magnitude of the problem has motivated numerous research studies around static or dynamic solutions to reduce the overall energy consumption of a data center. The aim of this thesis is to integrate renewable energy sources into dynamic energy optimization models in a data center. For this we use constraint programming as well as machine learning techniques. First, we propose a global constraint for tasks intersection that takes into account a ressource with variable cost. Second, we propose two learning models for the prediction of the work load of a data center and for the generation of such curves. Finally, we formalize the green energy aware scheduling problem (GEASP) and propose a global model based on constraint programming as well as a search heuristic to solve it efficiently. The proposed model integrates the various aspects inherent to the dynamic planning problem in a data center : heterogeneous physical machines, various application types (i.e., ractive applications and batch applications), actions and energetic costs of turning ON/OFF physical machine, interrupting/resuming batch applications, CPU and RAM ressource consumption of applications, migration of tasks and energy costs related to the migrations, prediction of green energy availability, variable energy consumption of physical machines.
Document type :
Theses
Complete list of metadatas

Cited literature [53 references]  Display  Hide  Download

https://tel.archives-ouvertes.fr/tel-01665187
Contributor : Abes Star <>
Submitted on : Friday, December 15, 2017 - 3:48:05 PM
Last modification on : Thursday, June 6, 2019 - 5:00:03 PM

File

2017IMTA0045_MadiWamba_Gilles....
Version validated by the jury (STAR)

Identifiers

  • HAL Id : tel-01665187, version 1

Citation

Gilles Madi Wamba. Combiner la programmation par contraintes et l’apprentissage machine pour construire un modèle éco-énergétique pour petits et moyens data centers. Recherche opérationnelle [cs.RO]. Ecole nationale supérieure Mines-Télécom Atlantique, 2017. Français. ⟨NNT : 2017IMTA0045⟩. ⟨tel-01665187⟩

Share

Metrics

Record views

393

Files downloads

527