CSE--SMA: Understanding the Performance of Modern Systems

CSE--SMA：了解现代系统的性能

• 批准号: 0509521
• 负责人: Amer Diwan
• 金额: $ 40万
• 依托单位: University of Colorado at Boulder
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2005
• 资助国家: 美国
• 起止时间: 2005-08-01 至 2010-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0509521&HistoricalAwards=false
• 关键词: CSE; SMA; Understanding; Performance; Modern

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.

建议的工作将开发、实现和评估有助于自动化现代软件系统性能分析的新技术。本文所追求的方法将自动化性能分析的问题分解为三个部分：识别性能异常、检测性能指标之间的协变以及确定协变指标之间的因果关系。提出的方法使用统计数据挖掘和机器学习技术来实现这三个组件的自动化。建议的系统处理一组跟踪，每个跟踪包含来自性能度量的一个或多个测量流。该项目将引入统计数据挖掘和机器学习技术来解决自动化性能分析问题。所提出的方法的基本见解是，在流的时变轮廓中存在重要的信息。以前用于性能分析的统计方法忽略了这些信息，而倾向于在特定的时间快照检查跨度量的协变。