ORBi: Geurts Pierre - A Machine Learning Approach to Improve Congestion Control over Wireless Computer Networks

Reference : A Machine Learning Approach to Improve Congestion Control over Wireless Computer Networks


	Document type :	Scientific congresses and symposiums : Paper published in a book
	Discipline(s) :	Engineering, computing & technology : Computer science
	To cite this reference:	http://hdl.handle.net/2268/4043

Title :	A Machine Learning Approach to Improve Congestion Control over Wireless Computer Networks
Language :	English
Author, co-author :	Geurts, Pierre [Université de Liège - ULg > Dép. d'électric., électron. et informat. (Inst.Montefiore) > Systèmes et modélisation >]
	El Khayat, Ibtissam [Université de Liège - ULg > Dép. d'électric., électron. et informat. (Inst.Montefiore) > Réseaux informatiques > > > > >]
	Leduc, Guy [Université de Liège - ULg > Dép. d'électric., électron. et informat. (Inst.Montefiore) > Réseaux informatiques >]
Publication date :	Nov-2004
Publisher :	IEEE
Pages :	383-386
On invitation :	No
Audience :	International
Country :	USA
Event name :	ICDM 2004
Event date :	1-4 Nov. 2004
Event place (city) :	Brighton
Event country :	UK
Keywords :	[en] Machine Learning ; Congestion control ; Wireless Networks
Abstract :	[en] In this paper, we present the application of machine learning techniques to the improvement of the congestion control of TCP in wired/wireless networks. TCP is suboptimal in hybrid wired/wireless networks because it reacts in the same way to losses due to congestion and losses due to link errors. We thus propose to use machine learning techniques to build automatically a loss classifier from a database obtained by simulations of random network topologies. Several machine learning algorithms are compared for this task and the best method for this application turns out to be decision tree boosting. It outperforms ad hoc classifiers proposed in the networking literature.
Funders :	Fonds de la Recherche Scientifique (Communauté française de Belgique) - F.R.S.-FNRS
Name of the research project :	PAI MOTION
Target :	Researchers
Permalink :	http://hdl.handle.net/2268/4043

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