PDL ABSTRACT

Storage Device Performance Prediction with CART Models [Extended Abstract]

Proceedings - Poster Session. Joint International Conference on Measurement and Modeling of Computer Systems ACM SIGMETRICS/Performance 2004. June 12th-16th 2004, Columbia University, New York.

Mengzhi Wang, Kinman Au, Anastassia Ailamaki, Anthony Brockwell, Christos Faloutsos, and Gregory R. Ganger

School of Computer Science
Carnegie Mellon University
5000 Forbes Ave.
Pittsburgh, PA 15213

http://www.pdl.cmu.edu/

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.

KEYWORDS: Performance prediction, Storage device modeling

EXTENDED ABSTRACT: pdf / ps

 

 

 

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