Ziad TLAISS Thesis defense

In recent years, the field of network troubleshooting has garnered significant attention from researchers due to the complexity and importance of this task. The work presented in this thesis focuses on automating network troubleshooting using performance metrics extracted from packet captures. The first contribution of this thesis lies in extracting features to identify the root cause of an anomaly by analyzing TCP packet traces with bad performance. We have categorized four frequently observed causes of degradation: transmission problems, congestion problems, jitter problems, and application-limited problems. The second contribution of this thesis involves developing an automated method to detect the moment of exiting the Slow-Start state. The significance of this method lies in saving valuable time in the analysis of network degradation, as the Slow-Start state serves as a key indicator for fault diagnosis. The third contribution of this thesis revolves around identifying the BBR congestion control algorithm. The primary goal of our approach is to detect whether packet pacing is employed in a TCP connection. This method relies on modeling the distribution of inter-packet duration during the Slow-Start state. The objective is to distinguish unimodal distributions of inter-packet intervals in the case of BBR compared to mixed two-component distributions in the case of CUBIC.