III: Medium:Simplifying Database Management with Automated Experimentation

尽管最近做出了许多努力，但数据库管理任务(如调优、故障排除、基准测试和容量规划)的当前解决方案仍然远远不能令人满意。数据库系统有许多配置参数来控制内存分配、I/O优化、查询计划的成本计算和其他行为。常规用户甚至是专业数据库管理员都很难调整这些参数以获得良好的性能。固有的复杂性使得这些系统很难优雅地处理不确定和动态变化的工作负载以及多样化的查询混合。.eX项目通过一种名为自动化实验驱动管理的新方法解决了这些挑战，该方法封装了关键构建块以自动生成系统行为知识，以简化管理和自我调整。这些构建块包括以下技术：(I)表征良好的系统性能模型；(Ii)计划生成数据的实验，以在系统和工作负载变化下有效地学习和维护这些模型；以及(Iii)考虑到模型捕获真实底层系统行为的准确性的内在不确定性，使用这些模型做出稳健的决策。除了系统管理，.eX的自动化实验驱动的管理还可以使MapReduce计算、大规模计算模拟和在线广告中的关键字拍卖等应用程序受益。杜克大学为研究生和本科生开设的两门新课程涵盖了自动化实验驱动的管理原理。正在开发一个功能齐全的.eX原型，并将其部署在多种环境中，以简化数据库管理。.eX的源代码将公开发布，该技术可能会移植到工业强度的系统管理产品上。.eX的结果将通过项目网站(http://www.cs.duke.edu/~shivnath/dotex.html).发布

Despite a number of recent efforts, current solutions for database administration tasks like tuning, troubleshooting, benchmarking, and capacity-planning remain far from satisfactory. Database systems have many configuration parameters to control memory distribution, I/O optimization, costing of query plans, and other behavior. Regular users and even expert database administrators struggle to tune these parameters for good performance. The inherent complexity makes it hard for these systems to gracefully handle uncertain and dynamically-changing workloads and diverse query mixes. The .eX project addresses these challenges through a novel methodology called automated experiment-driven management which encapsulates key building blocks to automatically generate knowledge of system behavior for simplified administration and self-tuning. These building blocks include techniques to: (i) characterize good system performance models, (ii) plan experiments that generate data to learn and maintain these models efficiently under system and workload changes, and (iii) make robust decisions using these models given the inherent uncertainty in how accurately the models capture the true underlying system behavior. Apart from system administration, .eX's automated experiment-driven management can benefit applications like MapReduce computations, large-scale computational simulations, and keyword auctions in online advertising. Two new courses for graduate and undergraduate students at Duke cover principles of automated experiment-driven management. A fully-functional prototype of .eX is being developed and deployed in multiple settings to simplify database administration. The source code of .eX will be released publicly and the technology will be migrated potentially to industrial-strength system administration products. Results from .eX will be disseminated via the project Web site (http://www.cs.duke.edu/~shivnath/dotex.html).