Reference : VoIP Traffic Modelling using Gaussian Mixture Models, Gaussian Processes and Interact...

	Document type :	Scientific congresses, symposiums and conference proceedings : Unpublished conference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/10993/23637

Title :	VoIP Traffic Modelling using Gaussian Mixture Models, Gaussian Processes and Interactive Particle Algorithms
Language :	English
Author, co-author :	Simionovici, Ana-Maria [University of Luxembourg > Faculty of Science, Technology and Communication (FSTC) > Computer Science and Communications Research Unit (CSC) >]
	Tantar, Alexandru [University of Luxembourg > Faculty of Science, Technology and Communication (FSTC) > Computer Science and Communications Research Unit (CSC)]
	Bouvry, Pascal [University of Luxembourg > Faculty of Science, Technology and Communication (FSTC) > Computer Science and Communications Research Unit (CSC) >]
	Tchernykh, Andrei [CICESE Research Center, Ensenada, Baja California, Mexico]
	Cortes-Mendoza, Jorge Mario [CICESE Research Center, Ensenada, Baja California, Mexico]
	Didelot, Loic [MIXvoip S.a. , Luxembourg]
Publication date :	5-Dec-2015
Number of pages :	6
Peer reviewed :	Yes
Audience :	International
Event name :	Globecom
Event date :	from 5-12-2015 to 10-12-2015
Event organizer :	IEEE
Event place (city) :	San Diego
Event country :	USA
Abstract :	[en] The paper deals with an important problem in the Voice over IP (VoIP) domain, namely being able to understand and predict the structure of traffic over some given period of time. VoIP traffic has a time variant structure, e.g. due to sudden peaks, daily or weekly moving patterns of activities, which in turn makes prediction difficult. Obtaining insights about the structure and trends of traffic has important implications when dealing with the nowadays cloud-deployed VoIP services. Prediction techniques are applied to anticipate the incoming traffic, for an efficient distribution of the traffic in the system and allocation of resources. The article looks in a critical manner at a series of machine learning techniques. We namely compare and review (using real VoIP data) the results obtained when using a Gaussian Mixture Model (GMM), Gaussian Processes (GP), and an evolutionary like Interacting Particle Systems based (sampling) algorithm. The experiments consider different setups as to verify the time variant traffic assumption.
Funders :	Fonds National de la Recherche - FnR ; CONACYT (Consejo Nacional de Ciencia y Tecnologa, Mexico)
Name of the research project :	DYMO
Permalink :	http://hdl.handle.net/10993/23637
FnR project :	FnR

	File(s) associated to this reference
	Fulltext file(s): File Commentary Version Size Access Open access 4.pdf Publisher postprint 558.64 kB View/Open

All documents in ORBi^lu are protected by a user license.

 

University of Luxembourg Library |  Feedback |  Legal notices Site Map