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

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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.