CSR---PDOS: Model-Driven Comprehensive Performance Anomaly Characterization for System Software

CSR---PDOS：模型驱动的系统软件综合性能异常表征

• 批准号: 0615045
• 负责人: Kai Shen
• 金额: $ 25.7万
• 依托单位: University of Rochester
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2006
• 资助国家: 美国
• 起止时间: 2006-09-01 至 2010-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0615045&HistoricalAwards=false
• 关键词: CSR; PDOS; Model; Driven; Comprehensive

Modern operating systems are large, complex software which are developed through collaboration of hundreds of people over years. They may perform anomalously due to various implementation problems, which cause performance degradation, and more importantly compromise the predictability of system performance behaviors. This project investigates a new approach to comprehensively examine the large space of possible runtime settings (including workload properties and system configurations) and to characterize performance anomalies in system software like operating systems. This approach relies on the knowledge of high-level system design algorithms/protocols and employs a system performance prediction model using such knowledge. Aided by the model, the approach acquires a representative set of anomalous workload and system configuration settings by comparing measured system performance with model prediction at some sample settings. Anomalous settings are then statistically clustered into groups likely attributed to individual ``causes''. Finally, each such cause (or performance bug) is characterized with correlated system configurations and workload properties. The research contribution of this project is the proposal of a systematic approach for comprehensively characterizing performance anomalies in complex system software. Such anomaly characterization can aid performance debugging and guide the avoidance of anomaly-inducing runtime settings. Broader impacts of the project include the dissemination of research results and developed software artifacts. In parallel to research, this project also enhances the systems-area curriculum at the University of Rochester, with the emphasis on having students recognize the existence of performance anomalies in complex systems and appreciate the difficulty to understand them.

现代操作系统是大型、复杂的软件，是由数百人多年合作开发的。 它们可能会由于各种实现问题而导致性能下降，更重要的是会损害系统性能行为的可预测性。 该项目研究了一种新的方法，以全面检查可能的运行时设置（包括工作负载属性和系统配置）的大空间，并表征操作系统等系统软件中的性能异常。 这种方法依赖于高级系统设计算法/协议的知识，并采用使用这种知识的系统性能预测模型。 在模型的帮助下，该方法通过在一些样本设置下将测量的系统性能与模型预测进行比较，获得一组具有代表性的异常工作负载和系统配置设置。 然后，异常设置被统计地聚类成可能归因于个体"原因“的组。 最后，每个这样的原因（或性能错误）的特点与相关的系统配置和工作负载属性。 该项目的研究贡献是提出了一种系统的方法，用于全面表征复杂系统软件中的性能异常。 这样的异常特征可以帮助性能调试，并指导避免异常诱导运行时设置。 该项目更广泛的影响包括研究成果和开发的软件工件的传播。 在研究的同时，该项目还加强了系统领域的课程在罗切斯特大学，重点是让学生认识到复杂系统中存在的性能异常，并了解理解它们的难度。