Sage：利用ML诊断云微服务中不可预测的性能
Sage: Leveraging ML to Diagnose Unpredictable Performance in Cloud Microservices
摘要：
云应用程序越来越多地从大型单体服务转向松散耦合的微服务的复杂图形。尽管微服务具有优势，但它也会在云应用程序中引入级联QoS违规，这很难诊断和纠正。我们介绍了Sage，一个ML驱动的交互式云微服务根本原因分析系统。Sage利用无监督学习模型来规避跟踪标记的开销，确定在线性能不可预测的根本原因，并应用纠正措施来恢复性能。在专用本地集群和大型GCE集群上的实验表明，Sage实现了高的根本原因检测精度和可预测的性能。
英文摘要：
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 cloud microservices. Sage leverages unsupervised learning models to circumvent the overhead of trace labeling, determines the root cause of unpredictable performance online, and applies corrective actions to restore performance. On experiments on both dedicated local clusters and large GCE clusters we show that Sage achieves high root cause detection accuracy and predictable performance.
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