CAREER: Capacity Planning Methodologies for Large Clusters with Heterogeneous Architectures and Diverse Applications

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This project focuses on developing innovative techniques and algorithms to build adequate system models and support performance and reliability analysis in order to better explain large system behavior, predict application performance, and ensure high resource efficiency and system dependability. Large cluster environments are an important part of today's computing infrastructure, providing the platform for running applications that handle core business and operational data. However, with the complexity of computing and application infrastructure increasing and the requirements for high quality of service growing, large cluster environments are facing the difficult task of ensuring that applications are always available and delivering adequate performance. This project expects to achieve new capacity planning techniques for performance modeling, workload measurements and model parameterizations of large cluster systems. Intelligent capacity and reliability modeling will enable service providers to determine the best platform for their application before deploying and running the application. It will also enable system managers to optimize the performance, reliability and efficiency of the entire cluster infrastructure. This research will develop new performance modeling methods to capture the characteristics of heterogeneous hardware architectures and predict the behavior of an application running on an array of computing platforms. The research will extend performance modeling to failure awareness. The improved models will enable an accurate prediction of performance and reliability of a complex large-scale system by capturing the characteristics of both system workloads and failure events. In addition, the researchers will develop new advanced techniques to parameterize performance models with essential processing information of computational and communication components. These essential processing information do not only limit to mean values but also include other critical yet complicated features such as resource contention and burstiness symptoms. The project is involved with educational activities reaching out to students from secondary to graduate schools to aggressively motivate students, especially women, towards science and engineering integrating this research into curriculum development and undergraduate research activities.