Sanaa GHANDI Thesis defense

The exponential growth of the Internet requires regular monitoring of network metrics. This thesis focuses on round-trip delays and the possibility of addressing the problems of missing data and multivariate segmentation. The first contribution includes the orchestration of delay measurement campaigns, as well as the development of a simulator that generates end-to-end delay traces. The second contribution of this thesis is the introduction of two missing data completion methods. The first is based on non-negative matrix factorization, while the second uses collaborative neural filtering. Tested on synthetic and real data, these methods demonstrate their efficiency and accuracy.
The third contribution of this thesis involves multivariate delay segmentation.
 This approach is based on hierarchical clustering and is implemented in two stages. Firstly, the delay time series are grouped to obtain, within the same group, series with similar and synchronous variations and trends. Next, the multivariate segmentation step collectively and jointly segments the series within each group. This step uses hierarchical clustering followed by post-processing using the Viterbi algorithm to smooth the segmentation result.This method was tested on real delay traces from two major events affecting two Internet Exchange Points (IXPs). The results show that this method approximates the state-of-the-art in segmentation, while significantly reducing computing speed and costs.