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Network troubleshooting is still a heavily human-intensive process. To reducethe time spent by human operators in the diagnosis process, we present a systembased on (i) unsupervised learning methods for detecting anomalies in the timedomain, (ii) an attention mechanism to rank features in the feature space andfinally (iii) an expert knowledge module able to seamlessly incorporatepreviously collected domain-knowledge. In this paper, we thoroughly evaluatethe 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 ofthe art in the respective fields. Leveraging a unique collection ofexpert-labeled datasets worth several months of real router telemetry data, weperform a thorough performance evaluation contrasting practical results inconstrained stream-mode settings, with the results achievable by an idealoracle in academic settings. Our experimental evaluation shows that (i) theproposed system is effective in achieving high levels of agreement with theexpert, and (ii) that even a simple statistical approach is able to extractuseful information from expert knowledge gained in past cases, significantlyimproving troubleshooting performance.

Human readable network troubleshooting based on anomaly detection and feature scoring