论文标题
Autothrottle：一种实用的双级方法，用于用于SLO靶向微服务的资源管理
Autothrottle: A Practical Bi-Level Approach to Resource Management for SLO-Targeted Microservices
论文作者
论文摘要
在保留最终用户体验的同时，对于云应用程序操作员来说，实现资源效率并不平淡。随着云应用程序逐渐采用微服务，资源管理人员面临着两个不同级别的系统行为：端到端应用程序延迟和每服务资源使用情况。但是，在这两个级别之间翻译是具有挑战性的，因为用户请求遍历（但不均匀）有助于端到端延迟的遍历异质服务。我们提出了Autothrottle，这是一个带有延迟SLO（服务级目标）的微服务的双层资源管理框架。它在架构上是将应用程序SLO反馈与服务资源控制的脱离，并通过性能目标概念桥接它们。具体而言，用于定期设置基于应用的学习控制器，以定期设置绩效目标（表示为CPU节气门比率），以实现每服务启发式控制器。我们在三个微服务应用程序上评估了Autothrottle，并具有生产方案的工作负载痕迹。结果显示，CPU节省优势，比表现最佳的基线高达26.21％，在所有基线上最多可达93.84％。
Achieving resource efficiency while preserving end-user experience is non-trivial for cloud application operators. As cloud applications progressively adopt microservices, resource managers are faced with two distinct levels of system behavior: end-to-end application latency and per-service resource usage. Translating between the two levels, however, is challenging because user requests traverse heterogeneous services that collectively (but unevenly) contribute to the end-to-end latency. We present Autothrottle, a bi-level resource management framework for microservices with latency SLOs (service-level objectives). It architecturally decouples application SLO feedback from service resource control, and bridges them through the notion of performance targets. Specifically, an application-wide learning-based controller is employed to periodically set performance targets -- expressed as CPU throttle ratios -- for per-service heuristic controllers to attain. We evaluate Autothrottle on three microservice applications, with workload traces from production scenarios. Results show superior CPU savings, up to 26.21% over the best-performing baseline and up to 93.84% over all baselines.
