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2012
IEEE

Tiresias: Online Anomaly Detection for Hierarchical Operational Network Data

6 years 7 months ago
Tiresias: Online Anomaly Detection for Hierarchical Operational Network Data
Operational network data, management data such as customer care call logs and equipment system logs, is a very important source of information for network operators to detect problems in their networks. Unfortunately, there is lack of efficient tools to automatically track and detect anomalous events on operational data, causing ISP operators to rely on manual inspection of this data. While anomaly detection has been widely studied in the context of network data, operational data presents several new challenges, including the volatility and sparseness of data, and the need to perform fast detection (complicating application of schemes that require offline processing or large/stable data sets to converge). To address these challenges, we propose Tiresias, an automated approach to locating anomalous events on hierarchical operational data. Tiresias leverages the hierarchical structure of operational data to identify high-impact aggregates (e.g., locations in the network, failure modes...
Chi-Yao Hong, Matthew Caesar, Nick G. Duffield, Ji
Added 29 Sep 2012
Updated 29 Sep 2012
Type Journal
Year 2012
Where ICDCS
Authors Chi-Yao Hong, Matthew Caesar, Nick G. Duffield, Jia Wang
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