  Performance and high availability have become increasingly important drivers,
amongst other drivers, for user retention in the context of web services such
as social networks, and web search. Exogenic and/or endogenic factors often
give rise to anomalies, making it very challenging to maintain high
availability, while also delivering high performance. Given that
service-oriented architectures (SOA) typically have a large number of services,
with each service having a large set of metrics, automatic detection of
anomalies 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 automatically
detecting anomalies in cloud infrastructure data. Specifically, the techniques
employ statistical learning to detect anomalies in both application, and system
metrics. Seasonal decomposition is employed to filter the trend and seasonal
components of the time series, followed by the use of robust statistical
metrics -- median and median absolute deviation (MAD) -- to accurately detect
anomalies, even in the presence of seasonal spikes.
  We demonstrate the efficacy of the proposed techniques from three different
perspectives, 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.
