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Predicting Internet Path Dynamics and Performance with Machine Learning
Wassermann, Sarah; Casas, Pedro; Cuvelier, Thibaut et al.


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Keywords :
Traceroute; Machine Learning; Prediction; Benchmarking; M-Lab; DTRACK
Abstract :
[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.
Disciplines :
Computer science
Author, co-author :
Wassermann, Sarah ;  Université de Liège - ULiège > Master sc. informatiques, à fin.
Casas, Pedro
Cuvelier, Thibaut ;  Université de Liège > Dép. d'électric., électron. et informat. (Inst.Montefiore) > Systèmes et modélisation : Optimisation discrète
Donnet, Benoît  ;  Université de Liège > Dép. d'électric., électron. et informat. (Inst.Montefiore) > Algorithmique des grands systèmes
Language :
Title :
Predicting Internet Path Dynamics and Performance with Machine Learning
Publication date :
April 2017
Publisher :
AIT Austria
Report number :
Name of the research project :
Big-DAMA: Big Data Analytics for network traffic Monitoring and Analysis
Available on ORBi :
since 11 April 2017


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