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Performance unpredictability in cloud services leads to poor user experience,degraded availability, and has revenue ramifications. Detecting performancedegradation a posteriori helps the system take corrective action, but does notavoid the QoS violations. Detecting QoS violations after the fact is even moredetrimental when a service consists of hundreds of thousands of loosely-coupledmicroservices, since performance hiccups can quickly propagate across thedependency graph of microservices. In this work we focus on anticipating QoSviolations in cloud settings to mitigate performance unpredictability to beginwith. We propose Seer, a cloud runtime that leverages the massive amount oftracing data cloud systems collect over time and a set of practical learningtechniques to signal upcoming QoS violations, as well as identify themicroservice(s) causing them. Once an imminent QoS violation is detected Seeruses machine-level hardware events to determine the cause of the QoS violation,and adjusts the resource allocations to prevent it. In local clusters with 1040-core servers and 200-instance clusters on GCE running diverse cloudmicroservices, we show that Seer correctly anticipates QoS violations 91% ofthe time, and attributes the violation to the correct microservice in 89% ofcases. Finally, Seer detects QoS violations early enough for a correctiveaction to almost always be applied successfully.

Seer: Leveraging Big Data to Navigate the Increasing Complexity of Cloud Debugging