Reference : Design of an Energy Efficiency Model and Architecture for Cloud Management using Pred...

	Document type :	Scientific congresses, symposiums and conference proceedings : Unpublished conference
	Discipline(s) :	Engineering, computing & technology : Computer science
	To cite this reference:	http://hdl.handle.net/10993/10183

Title :	Design of an Energy Efficiency Model and Architecture for Cloud Management using Prediction Models
Language :	English
Author, co-author :	Nguyen, Anh Quan [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > >]
	Tantar, Alexandru-Adrian [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > >]
	Bouvry, Pascal [University of Luxembourg > Faculty of Science, Technology and Communication (FSTC) > Computer Science and Communications Research Unit (CSC) >]
	Talbi, El-Ghazali []
Publication date :	18-Dec-2013
Number of pages :	4
Reviewed work :	SoCPaR-2013: The Fifth International Conference of Soft Computing and Pattern Recognition: "Innovating and Inspiring Soft Computing and Intelligent Pattern Recognition"
Peer reviewed :	Yes
On invitation :	No
Audience :	International
Event name :	SocPar 2013
Event date :	15-18 December 2013
Event organizer :	Le Quy Don Technical University
Event place (city) :	Hanoi
Event country :	Vietnam
Keywords :	[en] energy efficiency ; Gaussian Mixture Model ; OpenStack ; distributed and agent model ; power-aware
Abstract :	[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.
Research centres :	Interdisciplinary Centre for Security, Reliability and Trust
Funders :	Fonds National de la Recherche - FnR ; CNRS
Name of the research project :	I2R-DIR-PFN-12GRCL > FNR/INTER/CNRS/11/03 : GREEN@CLOUD > > BOUVRY Pascal
Target :	Researchers ; Students
Permalink :	http://hdl.handle.net/10993/10183

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