  Network troubleshooting is still a heavily human-intensive process. To reduce
the time spent by human operators in the diagnosis process, we present a system
based on (i) unsupervised learning methods for detecting anomalies in the time
domain, (ii) an attention mechanism to rank features in the feature space and
finally (iii) an expert knowledge module able to seamlessly incorporate
previously collected domain-knowledge. In this paper, we thoroughly evaluate
the performance of the full system and of its individual building blocks:
particularly, we consider (i) 10 anomaly detection algorithms as well as (ii)
10 attention mechanisms, that comprehensively represent the current state of
the art in the respective fields. Leveraging a unique collection of
expert-labeled datasets worth several months of real router telemetry data, we
perform a thorough performance evaluation contrasting practical results in
constrained stream-mode settings, with the results achievable by an ideal
oracle in academic settings. Our experimental evaluation shows that (i) the
proposed system is effective in achieving high levels of agreement with the
expert, and (ii) that even a simple statistical approach is able to extract
useful information from expert knowledge gained in past cases, significantly
improving troubleshooting performance.

Networking and Internet Architecture;Artificial Intelligence;Machine Learning