Reference : Design of an Energy Efficiency Model and Architecture for Cloud Management using Pred...
Scientific congresses, symposiums and conference proceedings : Unpublished conference
Engineering, computing & technology : Computer science
http://hdl.handle.net/10993/10183
Design of an Energy Efficiency Model and Architecture for Cloud Management using Prediction Models
English
Nguyen, Anh Quan mailto [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > >]
Tantar, Alexandru-Adrian mailto [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > >]
Bouvry, Pascal mailto [University of Luxembourg > Faculty of Science, Technology and Communication (FSTC) > Computer Science and Communications Research Unit (CSC) >]
Talbi, El-Ghazali mailto []
18-Dec-2013
4
SoCPaR-2013: The Fifth International Conference of Soft Computing and Pattern Recognition: "Innovating and Inspiring Soft Computing and Intelligent Pattern Recognition"
Yes
No
International
SocPar 2013
15-18 December 2013
Le Quy Don Technical University
Hanoi
Vietnam
[en] energy efficiency ; Gaussian Mixture Model ; OpenStack ; distributed and agent model ; power-aware
[en] In this paper, we present a new energy efficiency model and architecture for cloud management based on a prediction model with Gaussian Mixture Models. The methodology relies on a distributed agent model and the validation will be performed on OpenStack. This paper intends to be a position paper, the implementation and experimental run will be conducted in future work. The design concept leverages the prediction model by providing a full architecture binding the resource demands, the predictions and the actual cloud environment (Openstack). The prediction analysis feeds the power-aware agents that run on the compute nodes in order to turn the nodes into sleep mode when the load state is low to reduce the energy consumption of the data center.
Interdisciplinary Centre for Security, Reliability and Trust
Fonds National de la Recherche - FnR ; CNRS
Researchers ; Students
http://hdl.handle.net/10993/10183

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