Reference : A Machine Learning Approach to Improve Congestion Control over Wireless Computer Networks
Scientific congresses and symposiums : Paper published in a book
Engineering, computing & technology : Computer science
http://hdl.handle.net/2268/4043
A Machine Learning Approach to Improve Congestion Control over Wireless Computer Networks
English
Geurts, Pierre mailto [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 mailto [Université de Liège - ULg > Dép. d'électric., électron. et informat. (Inst.Montefiore) > Réseaux informatiques >]
Nov-2004
IEEE
383-386
Yes
No
International
USA
ICDM 2004
1-4 Nov. 2004
Brighton
UK
[en] Machine Learning ; Congestion control ; Wireless Networks
[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.
Fonds de la Recherche Scientifique (Communauté française de Belgique) - F.R.S.-FNRS
PAI MOTION
Researchers
http://hdl.handle.net/2268/4043
10.1109/ICDM.2004.10063

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