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 center :
Interdisciplinary Centre for Security, Reliability and Trust
Disciplines :
Computer science
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
Language :
English
Title :
Design of an Energy Efficiency Model and Architecture for Cloud Management using Prediction Models
Publication date :
18 December 2013
Number of pages :
4
Event name :
SocPar 2013
Event organizer :
Le Quy Don Technical University
Event place :
Hanoi, Vietnam
Event date :
15-18 December 2013
Audience :
International
References of the abstract :
SoCPaR-2013: The Fifth International Conference of Soft Computing and Pattern Recognition: "Innovating and Inspiring Soft Computing and Intelligent Pattern Recognition"