Predicting Internet Path Dynamics and Performance with Machine Learning

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.