CAREER: Querying and Controlling Systems

职业：查询和控制系统

• 批准号: 0644106
• 负责人: Shivnath Babu
• 金额: $ 50万
• 依托单位: Duke University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2007
• 资助国家: 美国
• 起止时间: 2007-02-01 至 2013-01-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0644106&HistoricalAwards=false
• 关键词: CAREER; Querying; Controlling; Systems

The increasing complexity, scale, and dynamics of networked computing systems make it hard for users and system administrators to understand and control these systems. A significant fraction of time and money is spent tackling unexpected system performance problems or tuning large systems with many components, the performance of which depends on thousands of dependencies and parameters. This problem is tackled by the Ques project using innovative data management techniques. Ques treats a computing system as a rich source of data about system configuration and activity, available typically as continuous, rapid, and time-varying data streams. System administrators are given the ability to pose a broad range of system management queries over this data. Ques addresses challenges in developing simple and intuitive ways to express these queries, processing the queries automatically and efficiently using query execution plans, and controlling systems based on statistical and performance models learned from system data. A fully functional prototype of Ques is developed and deployed in a real world setting. The ideas from Ques are incorporated into two new courses for graduate and undergraduate students at Duke. Automated plan generation algorithms for complex system management queries will have a major impact towards making systems more manageable by human administrators. The source code of Ques will be released publicly and the technology will be migrated potentially to industrial strength system management products. Results from Ques will be disseminated via the project Web site (http://www.cs.duke.edu/~shivnath/ques.html).

网络计算系统日益增长的复杂性、规模和动态化使得用户和系统管理员很难理解和控制这些系统。有很大一部分时间和金钱花在解决意外的系统性能问题或调优具有许多组件的大型系统上，这些组件的性能取决于数千个依赖项和参数。QUES项目使用创新的数据管理技术解决了这一问题。QUES将计算系统视为有关系统配置和活动的丰富数据源，通常作为连续、快速和时变的数据流可用。系统管理员可以对这些数据提出广泛的系统管理查询。QUES解决了以下方面的挑战：开发简单而直观的方法来表达这些查询，使用查询执行计划自动高效地处理查询，以及基于从系统数据学习的统计和性能模型控制系统。开发了一个功能齐全的QUES原型，并将其部署在真实世界的环境中。QUES的想法被纳入了杜克大学为研究生和本科生开设的两门新课程。用于复杂系统管理查询的自动计划生成算法将对使系统更易于由人类管理员进行管理产生重大影响。QUES的源代码将公开发布，该技术可能会移植到工业实力系统管理产品中。QUES的结果将通过项目网站(http://www.cs.duke.edu/~shivnath/ques.html).)发布