CSE--SMA: Understanding the Performance of Modern Systems

拟议的工作将开发，实施和评估有助于自动化现代软件系统的性能分析的新技术。所追求的方法分解成三个组成部分的自动化性能分析的问题：识别性能异常，检测性能指标之间的协变，并确定协变指标之间的因果关系。所提出的方法使用统计数据挖掘和机器学习技术来自动化这三个组成部分。建议的系统工作在一个跟踪的集合，每个跟踪包含一个或多个流的测量从一个性能metric.The项目将从统计数据挖掘和机器学习技术的技术来承担自动化性能分析的问题。所提出的方法的基本见解是，有显着的信息流的时变轮廓。 以前的性能分析的统计方法忽略了这些信息，而倾向于检查特定时间快照上指标之间的协变。

The proposed work will develop, implement, and evaluate new techniques that help to automate performance analysis of modern software systems. The methodology pursued breaks down the problem of automating performance analysis into three components: identifying performance anomalies, detecting covariation between performance metrics, and determining causality between covariant metrics. The proposed approach uses statistical data mining and machine learning techniques to automate the three components. The proposed system works on a collection of traces, with each trace containing one or more streams of measurements from a performance metric.The project will bring techniques from the statistical data mining and machine learning techniques to bear on the problem of automating performance analysis. The fundamental insight of the proposed approach is that there is significant information in the time-varying contours of a stream. Previous statistical approaches to performance analysis have ignored this information in favor of examining the covariation across metrics at a particular snapshot of time.