CSR: Small: Architecture-based Run-time Fault Diagnosis

CSR：小：基于架构的运行时故障诊断

• 批准号: 1116848
• 负责人: David Garlan
• 金额: $ 45万
• 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 2015-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1116848&HistoricalAwards=false
• 关键词: CSR; Small; Architecture; based; Run

Systems today must cope with failures induced by many factors outside the control of the organization producing the software: faults in infrastructure and components developed by third-parties, unpredictable loads, and variable resources. Modern systems must therefore take increasing responsibility for problem detection and repair at runtime. Effective fault detection and repair could be greatly enhanced by run-time fault diagnosis and localization -- the ability to identify the source of problem so that appropriate actions can be taken either by a human operator or automated mechanisms to repair the system. In this research we are developing new foundations for run-time fault diagnosis and localization. To do this we are extending and synthesizing recent advances in two areas. The first is the use of architecture models for monitoring a system at run-time. The second is the use of spectrum-based reasoning for fault localization (SFL). SFL is a lightweight technique that takes as its input a form of trace abstraction and produces a list of likely fault candidates, ordered by probability of being the true fault explanation. It has been used with impressive results during design time but thus far has not been exploited at runtime in the context of architecture-based monitoring and diagnosis. This research will improve the trustworthiness and robustness of modern software systems by providing new techniques for diagnosing faults while a system is running, thereby providing an improved basis for fault detection and resolution.

当今的系统必须应对由软件生产组织无法控制的许多因素引起的故障：第三方开发的基础设施和组件中的故障、不可预测的负载和可变资源。因此，现代系统必须承担越来越多的运行时问题检测和修复责任。运行时故障诊断和定位可以大大增强有效的故障检测和修复，即识别问题根源的能力，以便人工操作员或自动机制可以采取适当的措施来修复系统。在这项研究中，我们正在为运行时故障诊断和定位奠定新的基础。为此，我们正在扩展和综合两个领域的最新进展。第一个是使用架构模型在运行时监视系统。第二个是使用基于频谱的故障定位推理（SFL）。 SFL 是一种轻量级技术，它采用跟踪抽象形式作为输入，并生成可能的故障候选列表，并按真正故障解释的概率排序。它在设计时的使用取得了令人印象深刻的结果，但迄今为止尚未在基于体系结构的监视和诊断的背景下在运行时得到利用。这项研究将通过提供在系统运行时诊断故障的新技术来提高现代软件系统的可信度和鲁棒性，从而为故障检测和解决提供改进的基础。