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Storage Device Performance Prediction with CART Models (CMU-PDL-04-103)

journal contribution
posted on 01.03.2004, 00:00 by Mengzhi Wang, Kinman Au, Anastassia Ailamaki, Anthony Brockwell, Christos Faloutsos, Gregory R. Ganger
Storage device performance prediction is a key element of self-managed storage systems. This work explores the application of a machine learning tool, CART models, 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 a good interpolation across different workloads.


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