Seer: leveraging big data to navigate the complexity of performance debugging in cloud microservices
Seer: leveraging big data to navigate the complexity of performance debugging in cloud microservices Gan et al., ASPLOS’19
Last time around we looked at the DeathStarBench suite of microservices-based benchmark applications and learned that microservices systems can be especially latency sensitive, and that hotspots can propagate through a microservices architecture in interesting ways. Seer is an online system that observes the behaviour of cloud applications (using the DeathStarBench microservices for the evaluation) and predicts when QoS violations may be about to occur. By cooperating with a cluster manager it can then take proactive steps to avoid a QoS violation occurring in practice.
We show that Seer correctly anticipates QoS violations 91% of the time, and avoids the QoS violation to begin with in 84% of cases. Finally, we show that Seer can identify application level design bugs, and provide insights on how to better architect microservices to achieve predictable performance.
Seer uses a lightweight RPC-level tracing system to collect request traces and aggregate them in a Cassandra database. A DNN model is trained to recognise patterns in space and time that lead to QoS violations. This model makes predictions at runtime based on real-time streaming trace inputs. When a Continue reading

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