CSR: Small: Telescopic Analysis for Black-Box Troubleshooting of Distributed Systems

CSR：小型：分布式系统黑盒故障排除的伸缩分析

• 批准号: 1421441
• 负责人: Jason Flinn
• 金额: $ 49.82万
• 依托单位: Regents of the University of Michigan - Ann Arbor
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-09-01 至 2018-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1421441&HistoricalAwards=false
• 关键词: CSR; Small; Telescopic; Analysis; Black

This project is developing scalable mechanisms to debug, monitor, andassess the quality of the complex distributed systems that representthe backbone of modern software infrastructure. These methods arenecessarily highly-automated; they reason about the operation ofdistributed systems while treating the components of such systems asblack boxes. This means that the methods do not require source code,programmer annotation, or developer input to troubleshoot adistributed system. Instead, they rely on detailed informationgleaned from pre-existing log messages that are nearly ubiquitous inevery large-scale distributed system and data extracted via binaryanalysis of components as they run.These new methods, termed telescopic analysis, combine the ability tocollect extremely detailed, low-level information about systemsexecuting large numbers of requests with "big data" analysis thatmines insights and create models of system operation from the corpusof detailed observations. Telescopic analysis uses targeted,sample-based logging and/or binary analysis to generate substantialquantities of high-precision data about specific runs of the systemunder observation. It then combines these observations into modelsthat capture the aggregate behavior of the system. Comparing thegeneral model with the detailed observations of each run allowsunderstanding of how that run conforms to or deviates from the commonoperation of the system. The project is also developing tools andquery languages that allow understanding of the results of suchcomparisons, both in aggregate and as pertains to specific runs, forperformance analysis, debugging data quality failures, understandingoutlier behavior, and performing "what-if" analysis.

该项目正在开发可扩展的机制，以调试、监控和评估代表现代软件基础设施主干的复杂分布式系统的质量。这些方法必然是高度自动化的；它们对分布式系统的操作进行推理，同时将此类系统的组件视为黑匣子。这意味着这些方法不需要源代码、程序员注释或开发人员输入即可对分布式系统进行故障排除。取而代之的是，它们依赖于从每个大型分布式系统中几乎无处不在的预先存在的日志消息中获取的详细信息，以及通过对运行时的组件进行二进制分析而提取的数据。这些称为望远镜分析的新方法将收集有关执行大量请求的系统的极详细、低级别信息的能力与“大数据”分析结合在一起，该分析挖掘洞察并根据详细观察语料库创建系统操作模型。望远镜分析使用有针对性的、基于样本的记录和/或二进制分析，以生成大量关于被观察系统的具体运行的高精度数据。然后，它将这些观察结果合并到模型中，以捕获系统的聚合行为。将一般模型与每一次运行的详细观察相比较，可以了解该运行如何符合或偏离系统的常见操作。该项目还在开发工具和查询语言，以便在总体上和与具体运行有关的情况下了解这类比较的结果，以便进行性能分析、调试数据质量故障、了解异常行为和执行“假设分析”。