  Large-scale monitoring, anomaly detection, and root cause analysis of metrics
are essential requirements of the internet-services industry. To address the
need to continuously monitor millions of metrics, many anomaly detection
approaches are being used on a daily basis by large internet-based companies.
However, in spite of the significant progress made to accurately and
efficiently detect anomalies in metrics, the sheer scale of the number of
metrics has meant there are still a large number of false alarms that need to
be investigated. This paper presents a framework for reliable large-scale
anomaly detection. It is significantly more accurate than existing approaches
and allows for easy interpretation of models, thus enabling practical data
monitoring in the internet-services domain.
