  Round-Trip Times are one of the most commonly collected performance metrics
in computer networks. Measurement platforms such as RIPE Atlas provide
researchers and network operators with an unprecedented amount of historical
Internet delay measurements. It would be very useful to automate the processing
of these measurements (statistical characterization of paths performance,
change detection, recognition of recurring patterns, etc.). Humans are pretty
good at finding patterns in network measurements but it can be difficult to
automate this to enable many time series being processed at the same time. In
this article we introduce a new model, the HDP-HMM or infinite hidden Markov
model, whose performance in trace segmentation is very close to human
cognition. This is obtained at the cost of a greater complexity and the
ambition of this article is to make the theory accessible to network monitoring
and management researchers. We demonstrate that this model provides very
accurate results on a labeled dataset and on RIPE Atlas and CAIDA MANIC data.
This method has been implemented in Atlas and we introduce the publicly
accessible Web API.
