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ABSTRACTCarnegie Mellon, School of Computer ScienceStorage Device Performance Prediction with CART Models Mengzhi Wang, Kinman Au, Anastassia Ailamaki, Anthony Brockwell, Christos Faloutsos, and Gregory R. Ganger Carnegie Mellon University Storage device performance prediction is a key element of self-managed storage systems and application planning tasks, such as data assignment. This work explores the application of a machine learning tool, CART mod els, to storage device modeling. Our approach predicts a device's performance as a function of input workloads, requiring no knowledge of the device internals. We propose two uses of CART models: one that predicts per-request response times (and then derives aggregate values) and one that predicts aggregate values directly from workload characteristics. After being trained on the device in question, both provide accurate black-box models across a range of test traces from real environments. Experiments show that these models predict the average and 90th percentile response time with an relative error as low as 19%, when the training workloads are similar to the testing workloads, and interpolate well across different workloads. FULL PAPER: pdf |