A Machine Learning Approach to Improve Congestion Control over Wireless Computer Networks
, I. El Khayat2 and G. Leduc2
1 Stochastic Methods research unit, EECS department, University of Liège, Belgium
2 Research unit in Networking, EECS department, University of Liège, Belgium
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.