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KDD 2020 | LUMOS: A library for diagnosing metric regressions for web-scale applications

Accepted Papers

LUMOS: A library for diagnosing metric regressions for web-scale applications

Jamie Pool: Microsoft; Ebrahim Beyrami: Microsoft; Vishak Gopal: Microsoft; Jayant Gupchup: Microsoft; Jeff Rowland: HCL America Inc; Binlong Li: Microsoft; Pritesh Kanani: Microsoft; Ross Cutler: Microsoft; Johannes Gehrke: Microsoft; Ashkan Aazami: Microsoft


Web-scale applications can ship code on a daily to weekly cadence. These applications rely on online metrics to monitor the health of new releases. Regressions in metric values need to be detected and diagnosed as early as possible to reduce the disruption to users and product owners. Regressions in metrics can surface due to a variety of reasons: genuine product regressions, changes in user population and bias due to telemetry loss (or processing) are among the common causes. Diagnosing the cause of these metric regressions is costly for engineering teams as they need to invest time in finding the root cause of the issue as soon as possible. We presentLumos, a Python library built using the principles of A/B testing to systematically diagnose metric regressions to automate such analysis.Lumos has been deployed across the component teams in Microsoft’s Real-Time Communication (RTC) applications Skype and Microsoft Teams. It has enabled engineering teams to detect 100s of real changes in metrics and reject 1000s of false alarms detected by anomaly detectors. The application ofLumos has resulted in freeing up as much as $95%$ of the time allocated to metric-based investigations. In this work, we open sourceLumos and present our results from applying it to two different components within the RTC group over millions of sessions. This general library can be coupled with any production system to manage the volume of alerting efficiently.

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