CAREER: Querying and Controlling Systems

网络计算系统日益增长的复杂性、规模和动态性使得用户和系统管理员很难理解和控制这些系统。大量的时间和金钱花费在处理意外的系统性能问题或调优具有许多组件的大型系统上，这些组件的性能依赖于成千上万的依赖项和参数。Ques项目使用创新的数据管理技术来解决这个问题。Ques将计算系统视为关于系统配置和活动的丰富数据源，通常以连续、快速和时变的数据流的形式提供。系统管理员可以对这些数据进行广泛的系统管理查询。Ques解决了以下方面的挑战：开发简单直观的方式来表达这些查询，使用查询执行计划自动有效地处理查询，以及基于从系统数据中学习的统计和性能模型来控制系统。开发了一个功能齐全的Ques原型，并在现实环境中进行了部署。奎斯的想法被纳入杜克大学面向研究生和本科生的两门新课程中。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管理员管理产生重大影响。Ques的源代码将公开发布，该技术将潜在地迁移到工业实力的系统管理产品中。问题的结果将通过项目网站（http://www.cs.duke.edu/~shivnath/ques.html）传播。

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).