Storage device performance prediction with CART models

d Abstract] Mengzhi Wang, Kinman Au, Anastassia Ailamaki, Anthony Brockwell, Christos Faloutsos, and Gregory R. Ganger Carnegie Mellon University 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 a range of test traces from real environments. An expanded version of this paper is available as a technical report [1]. Categories and Subject Descriptors C.4 [Performance of systems]: Modeling techniques; D.4.8 [Performance]: Modeling and Prediction General Terms Experimentation,Performance,Management Keyw...

• Type: Conference
• Year: 2004
• Where: SIGMETRICS
• Authors: Mengzhi Wang, Kinman Au, Anastassia Ailamaki, Anthony Brockwell, Christos Faloutsos, Gregory R. Ganger
• Keywords: Aggregate Values, Prediction, SIGMETRICS 2004, Storage Device Modeling