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Cloud applications are increasingly shifting from large monolithic services,to complex graphs of loosely-coupled microservices. Despite their advantages,microservices also introduce cascading QoS violations in cloud applications,which are difficult to diagnose and correct.  We present Sage, a ML-driven root cause analysis system for interactive cloudmicroservices. Sage leverages unsupervised learning models to circumvent theoverhead of trace labeling, determines the root cause of unpredictableperformance online, and applies corrective actions to restore performance. Onexperiments on both dedicated local clusters and large GCE clusters we showthat Sage achieves high root cause detection accuracy and predictableperformance.

Sage: Leveraging ML to Diagnose Unpredictable Performance in Cloud Microservices