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.

该项目正在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的日益复杂，这使得计算机系统难以配置和正确管理的动机。 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，技术支持估计占台式计算机总拥有成本的17%，占信息系统总拥有成本的60-80%。我们正在演示因果关系跟踪的系统支持如何大大减少软件故障排除所需的时间和人力。 我们关注的是配置问题，在这种情况下，应用程序代码是正确的，但软件的安装、配置或更新不正确，因此它的行为不符合预期。 我们正在开发自动化故障排除的方法和工具，从而减少从错误中恢复的时间，并减少手动工作量。 我们的工具通过使用动态检测来监控字节粒度的信息流，从而跟踪软件二进制文件中的因果关系。 它们在文件、进程和多台计算机之间传播此信息，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的软件程序的控制流的配置值和其他输入的集合。 我们希望在这个项目中开发的工具将使复杂的计算机系统更容易管理，这有可能大大降低我们国家的计算机基础设施的行政支持成本。