Reference : Machine Learning based Prediction of Internet Path Dynamics
Scientific congresses and symposiums : Paper published in a book
Engineering, computing & technology : Computer science
Machine Learning based Prediction of Internet Path Dynamics
Wassermann, Sarah mailto [Université de Liège - ULiège > > > Master sc. informatiques, à fin.]
Casas, Pedro []
Donnet, Benoît mailto [Université de Liège > Dép. d'électric., électron. et informat. (Inst.Montefiore) > Algorithmique des grands systèmes >]
ACM CoNEXT Student Workshop: Irvine 12 décembre 2016
ACM CoNEXT Student Workshop
12 décembre 2016
[en] Traceroute ; Change Prediction ; Machine Learning
[en] We study the problem of predicting Internet path changes and path performance using traceroute and machine-learning techniques. Path changes are frequently linked to path inflation and performance degradation. Therefore, predicting their occurrence could improve the analysis of path dynamics using traceroute. By relying on neural networks and using empirical distribution based input features, we show that we are able to predict (i) the remaining life time of a path before it actually changes, and (ii) the number of path changes in a certain time slot with relatively high accuracy. We also show that it is possible to predict path performance in terms of latency, opening the door to novel, machine-learning-based approaches for RTT prediction.

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