MESO: Supporting Online Decision Making in Autonomic Computing Systems
IEEE Transactions on Knowledge and Data Engineering
Design and preliminary study of the W-PRDR: a new congestion control scheme for wireless networks
Proceedings of the 1st international conference on Simulation tools and techniques for communications, networks and systems & workshops
Improving TCP performance in wireless networks by classifying causes of packet losses
WCNC'09 Proceedings of the 2009 IEEE conference on Wireless Communications & Networking Conference
GIIS'09 Proceedings of the Second international conference on Global Information Infrastructure Symposium
Improving TCP in wireless networks with an adaptive machine-learnt classifier of packet loss causes
NETWORKING'05 Proceedings of the 4th IFIP-TC6 international conference on Networking Technologies, Services, and Protocols; Performance of Computer and Communication Networks; Mobile and Wireless Communication Systems
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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 sub-optimal 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.