CSR: Small: Distributed System Diagnosis via Request Flow Comparison

CSR：小型：通过请求流比较进行分布式系统诊断

• 批准号: 1117567
• 负责人: Gregory Ganger
• 金额: $ 48.78万
• 依托单位: Carnegie-Mellon University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2011
• 资助国家: 美国
• 起止时间: 2011-08-01 至 2014-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1117567&HistoricalAwards=false
• 关键词: CSR; Small; Distributed; System; Diagnosis

This work builds on recent instrumentation approaches that provide low-overhead end-to-end tracing (e.g., Stardust and Dapper) that can capture the flow (i.e., path and timing) of individual requests within and across the components of a distributed system. In addition to tools for profiling and examining system behavior, the PIs are creating tools that compare request flows between two executions to guide understanding of changes in performance, as contrasted with determining why a system has always been slow. Comparing request flows can help diagnose a large class of performance problems, such as degradations resulting from software changes/upgrades or from usage over time (e.g., due to resource leakage or workload changes). This research is evaluating the efficacy of this approach, addressing scalability challenges in instrumentation data collection and analysis, and exploring the effects of virtualization on variability.

这项工作建立在最近提供低开销的端到端跟踪（例如，Stardust和Dapper）的仪器方法的基础上，这些方法可以捕获分布式系统组件内部和跨组件的单个请求的流（即，路径和时间）。除了用于分析和检查系统行为的工具之外，pi还创建了用于比较两次执行之间的请求流的工具，以指导对性能变化的理解，而不是确定系统总是很慢的原因。比较请求流可以帮助诊断一大类性能问题，例如由于软件更改/升级或由于长时间使用（例如，由于资源泄漏或工作负载更改）而导致的性能下降。本研究评估了这种方法的有效性，解决了仪器数据收集和分析中的可伸缩性挑战，并探索了虚拟化对可变性的影响。