We demonstrate a near real-time service monitoring system for detecting and diagnosing issues from high-dimensional time series data. For detection, we have implemented a learning algorithm that constructs a hierarchy of detectors from data. It is scalable, does not require labelled examples of issues for learning, runs in near real-time, and identifies a subset of counter time series as being relevant for a detected issue. For diagnosis, we provide efficient algorithms as post-detection diagnosis aids to find further relevant counter time series at issue times, a SQL-like query language for writing flexible queries that apply these algorithms on the time series data, and a graphical user interface for visualizing the detection and diagnosis results. Our solution has been deployed in production as an end-to-end system for monitoring Microsoft\u2019s internal distributed data storage and computing platform consisting of tens of thousands of machines and currently analyses about 12000 counter time series<\/p>\n","protected":false},"excerpt":{"rendered":"
We demonstrate a near real-time service monitoring system for detecting and diagnosing issues from high-dimensional time series data. For detection, we have implemented a learning algorithm that constructs a hierarchy of detectors from data. It is scalable, does not require labelled examples of issues for learning, runs in near real-time, and identifies a subset of 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