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Achieving resource efficiency while preserving end-user experience isnon-trivial for cloud application operators. As cloud applicationsprogressively adopt microservices, resource managers are faced with twodistinct levels of system behavior: end-to-end application latency andper-service resource usage. Translating between the two levels, however, ischallenging because user requests traverse heterogeneous services thatcollectively (but unevenly) contribute to the end-to-end latency. We presentAutothrottle, a bi-level resource management framework for microservices withlatency SLOs (service-level objectives). It architecturally decouplesapplication SLO feedback from service resource control, and bridges themthrough the notion of performance targets. Specifically, an application-widelearning-based controller is employed to periodically set performance targets-- expressed as CPU throttle ratios -- for per-service heuristic controllers toattain. We evaluate Autothrottle on three microservice applications, withworkload traces from production scenarios. Results show superior CPU savings,up to 26.21% over the best-performing baseline and up to 93.84% over allbaselines.

Autothrottle: A Practical Bi-Level Approach to Resource Management for SLO-Targeted Microservices