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Performance and high availability have become increasingly important drivers,amongst other drivers, for user retention in the context of web services suchas social networks, and web search. Exogenic and/or endogenic factors oftengive rise to anomalies, making it very challenging to maintain highavailability, while also delivering high performance. Given thatservice-oriented architectures (SOA) typically have a large number of services,with each service having a large set of metrics, automatic detection ofanomalies is non-trivial.  Although there exists a large body of prior research in anomaly detection,existing techniques are not applicable in the context of social network data,owing to the inherent seasonal and trend components in the time series data.  To this end, we developed two novel statistical techniques for automaticallydetecting anomalies in cloud infrastructure data. Specifically, the techniquesemploy statistical learning to detect anomalies in both application, and systemmetrics. Seasonal decomposition is employed to filter the trend and seasonalcomponents of the time series, followed by the use of robust statisticalmetrics -- median and median absolute deviation (MAD) -- to accurately detectanomalies, even in the presence of seasonal spikes.  We demonstrate the efficacy of the proposed techniques from three differentperspectives, viz., capacity planning, user behavior, and supervised learning.In particular, we used production data for evaluation, and we report Precision,Recall, and F-measure in each case.

Automatic Anomaly Detection in the Cloud Via Statistical Learning