CSR: Small: System Support for Causality-Driven Automated Troubleshooting

CSR：小型：对因果关系驱动的自动故障排除的系统支持

• 批准号: 1017148
• 负责人: Jason Flinn
• 金额: $ 49.95万
• 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: 2010
• 资助国家: 美国
• 起止时间: 2010-09-01 至 2015-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1017148&HistoricalAwards=false
• 关键词: CSR; Small; System; Support; Causality

This project is building tools and developing methods that identify the root cause of software configuration problems and suggest potential corrective actions. Our work is motivated by the increasing complexity of modern software, which makes computer systems difficult to configure and manage correctly. Users and administrators currently spend considerable time and effort troubleshooting software configuration problems. For instance, technical support is estimated to contribute 17% of the total cost of ownership for desktop computers and 60-80% for information systems.We are demonstrating how system support for causality tracking can substantially reduce the time and human effort needed to troubleshoot software. We are focusing on configuration problems, in which the application code is correct, but the software has been installed, configured, or updated incorrectly so that it does not behave as desired. We are developing methods and tools that automate troubleshooting, thereby reducing the time to recover from errors and requiring less manual effort. Our tools track causality within software binaries by using dynamic instrumentation to monitor information flow at byte granularity. They propagate this information among files, processes, and multiple computers to troubleshoot complex distributed systems. Multi-level causality tracking helps determine the set of configuration values and other inputs that are most likely to have influenced the control flow of misconfigured software programs. We expect that the tools developed during this project will make complex computer systems easier to manage; this has the potential to dramatically reduce administrative support costs for our nation's computer infrastructure.

该项目是构建工具和开发方法，以识别软件配置问题的根本原因并提出潜在的纠正措施。 我们的工作是由现代软件越来越复杂的激励，这使得计算机系统难以配置和正确管理。 用户和管理员目前花费大量时间和精力来解决软件配置问题。 例如，估计技术支持占台式计算机总拥有成本的17％和信息系统的60-80％。我们正在证明系统对因果关系跟踪的支持如何大大减少软件解决问题所需的时间和人类努力。 我们关注的是配置问题，其中应用程序代码正确，但是已安装，配置或更新的软件是错误的，因此它的行为不按需要进行。 我们正在开发自动化故障排除的方法和工具，从而减少了从错误中恢复并需要减少手动努力的时间。 我们的工具通过使用动态仪器来监视字节粒度的信息流来跟踪软件二进制文件中的因果关系。 他们在文件，过程和多台计算机之间传播这些信息，以对复杂的分布式系统进行故障排除。多级因果关系跟踪有助于确定最有可能影响错误配置软件程序的控制流的配置值和其他输入的集合。 我们预计该项目期间开发的工具将使复杂的计算机系统易于管理；这有可能大大降低国家计算机基础设施的行政支持成本。