Storage Device Performance Prediction with CART Models (Extended Abstract)
Mengzhi Wang, Kinman Au, Anastassia Ailamaki, Anthony Brockwell, Christos Faloutsos, Gregory R. Ganger · 2004
This work explores the application of a machine learning tool, CART modeling, to storage devices. We have developed approaches to predict a device’s performance as a function of input workloads, requiring no knowledge of the device internals. Two uses of CART models are considered: one that predicts per-request response times (and then derives aggregate values) and one that predicts aggregate values directly from workload characteristics. After training on the device in question, both provide reasonably-accurate black box models across ar ange of test traces from real environments. An expanded version of this paper is available as a technical report [1].