CSR-SMA: Measurement, Modeling and Analysis of Large Complex Data Centers

近年来，企业数据中心的复杂性稳步增长，这主要是由于系统硬件和软件的小增量变化导致的持续演变。 该项目的重点是提高复杂数据中心的可管理性的建模和分析方法。 该项目的重点是（i）超大型数据中心的可扩展自适应监控技术，（ii）复杂数据中心应用程序的多尺度建模，以及（iii）模型驱动分析，以预测系统性能，性能和容量需求。 自适应监控方法涉及使用统计数据挖掘和学习来动态地改变什么系统被监控以及何时被监控。 多尺度模型涉及通过在不同抽象级别（数据中心级别、应用程序级别和单个服务器级别）对应用程序交互进行建模，从而在大、中、小尺度上捕获系统行为。模型驱动分析将自适应监控和多尺度模型结合在一起，用于容量调配、瓶颈检测和性能预测。建模和分析技术将使用运行在基于Linux的数据中心测试平台上的真实数据中心应用程序进行验证。更广泛的影响包括源代码发布和匿名跟踪数据的共享，以使其他研究人员能够进行进一步的实验，并通过夏季REU项目让本科生参与这项研究。

The complexity of enterprise data centers has grown steadily in recentyears, primarily due to continuous evolution resulting from smallincremental changes to system hardware and software. This projectfocuses on a modeling and analysis approach for improving themanageability of complex data centers. The project focuses on (i)scalable, adaptive monitoring techniques for very large data centers,(ii) multi-scale modeling of complex data-center applications, and(iii) model-driven analysis to predict system performance, bottlenecksand capacity requirements. The adaptive monitoring approach involvesthe use of statistical data mining and learning to dynamically varywhat systems are monitored and when. The multi-scale models involvecapturing system behavior at large, medium and small scales bymodeling application interactions at different levels of abstraction:at the data center level, at the application level, and at theindividual server level. Model-driven analysis brings togetheradaptive monitoring and the multi-scale models for capacityprovisioning, bottleneck detection and performance prediction. Themodeling and analysis techniques will be validated using real-worlddata center applications running on a Linux-based data center testbed.Broader impacts include source code release and sharing of anonymizedtrace data to enable further experimentation by other researchers andthe participation of undergraduate students in this research viasummer REU projects.