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Operational network data, management data such as customer care call logs andequipment system logs, is a very important source of information for networkoperators to detect problems in their networks. Unfortunately, there is lack ofefficient tools to automatically track and detect anomalous events onoperational data, causing ISP operators to rely on manual inspection of thisdata. While anomaly detection has been widely studied in the context of networkdata, operational data presents several new challenges, including thevolatility and sparseness of data, and the need to perform fast detection(complicating application of schemes that require offline processing orlarge/stable data sets to converge).  To address these challenges, we propose Tiresias, an automated approach tolocating anomalous events on hierarchical operational data. Tiresias leveragesthe hierarchical structure of operational data to identify high-impactaggregates (e.g., locations in the network, failure modes) likely to beassociated with anomalous events. To accommodate different kinds of operationalnetwork data, Tiresias consists of an online detection algorithm with low timeand space complexity, while preserving high detection accuracy. We presentresults from two case studies using operational data collected at a largecommercial IP network operated by a Tier-1 ISP: customer care call logs andset-top box crash logs. By comparing with a reference set verified by the ISP'soperational group, we validate that Tiresias can achieve >94% accuracy inlocating anomalies. Tiresias also discovered several previously unknownanomalies in the ISP's customer care cases, demonstrating its effectiveness.

Tiresias: Online Anomaly Detection for Hierarchical Operational Network Data