Reference : Predicting Internet Path Dynamics and Performance with Machine Learning
Reports : External report
Engineering, computing & technology : Computer science
http://hdl.handle.net/2268/209422
Predicting Internet Path Dynamics and Performance with Machine Learning
English
Wassermann, Sarah mailto [Université de Liège - ULiège > > > Master sc. informatiques, à fin.]
Casas, Pedro []
Cuvelier, Thibaut mailto [Université de Liège > Dép. d'électric., électron. et informat. (Inst.Montefiore) > Systèmes et modélisation : Optimisation discrète >]
Donnet, Benoît mailto [Université de Liège > Dép. d'électric., électron. et informat. (Inst.Montefiore) > Algorithmique des grands systèmes >]
Apr-2017
AIT Austria
[en] Traceroute ; Machine Learning ; Prediction ; Benchmarking ; M-Lab ; DTRACK
[en] In this paper, we study the problem of predicting Internet path changes and path performance using traceroute measurements and machine learning models. Path changes are frequently linked to path inflation and performance degradation; therefore, predicting their occurrence is highly relevant for performance monitoring and dynamic traffic engineering. We introduce NETPerfTrace, an Internet Path Tracking system capable of forecasting path changes and path latency variations. By relying on decision trees and using empirical distribution based input features, we show that NETPerfTrace can 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 high accuracy. Through extensive evaluation, we demonstrate that NETPerfTrace highly outperforms DTRACK, a previous system with the same prediction targets. NETPerfTrace also offers path performance forecasting capabilities. In particular, it can predict path latency metrics, providing a system which could not only predict path changes but also forecast their impact in terms of performance variations. As an additional contribution, we release NETPerfTrace as open software to the networking community.
Big-DAMA: Big Data Analytics for network traffic Monitoring and Analysis
Researchers
http://hdl.handle.net/2268/209422

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