Anomaly root cause diagnosis from active and passive measurement analysis

Diagnosis demands a deep analysis of data to identify the root cause of an anomaly and still mostly relies on human experts. The increase of Internet traffic combined with the arrival of the encrypted protocol QUIC which invalidates many troubleshooting methods, urges to automate this process. To this effect, both domain familiarity and analysis skills are required. In this work we present our methods and strategies to detect the root cause of anomalies from active and passive network measurement and we share our plan towards an automatic root cause diagnosis. We focus on four root causes : transmission, congestion, application limited and packet delay variation, and present the building blocks of classification methods.