Improving TCP in Wireless Networks with an Adaptive Machine-Learnt Classifier of Packet Loss Causes

  • Ibtissam El Khayat
  • Pierre Geurts
  • Guy Leduc
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3462)

Abstract

TCP understands all packet losses as buffer overflows and reacts to such congestions by reducing its rate. In hybrid wired/wireless networks where a non negligible number of packet losses are due to link errors, TCP is unable to sustain a reasonable rate. In this paper, we propose to extend TCP Newreno with a packet loss classifier built by a supervised learning algorithm called ’decision tree boosting’. The learning set of the classifier is a database of 25,000 packet loss events in a thousand of random topologies. Since a limited percentage of wrong classifications of congestions as link errors is allowed to preserve TCP-Friendliness, our protocol computes this constraint dynamically and tunes a parameter of the classifier accordingly to maximise the TCP rate. Our classifier outperforms the Veno and Westwood classifiers by achieving a higher rate in wireless networks while remaining TCP-Friendly.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Ibtissam El Khayat
    • 1
  • Pierre Geurts
    • 1
  • Guy Leduc
    • 1
  1. 1.Department of Electrical Engineering and Computer ScienceUniversity of Liège, Institut MontefioreLiègeBelgium

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