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Storage Device Performance Prediction with CART Models Mengzhi Wang, Kinman Au, Anastassia Ailamaki,
 

Summary: Storage Device Performance Prediction with CART Models
Mengzhi Wang, Kinman Au, Anastassia Ailamaki,
Anthony Brockwell, Christos Faloutsos, and Gregory R. Ganger
Carnegie Mellon University, Pittsburgh, PA 15213 USA
Abstract
Storage device performance prediction is a key element
of self-managed storage systems and application plan-
ning 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 pro-
pose two uses of CART models: one that predicts per-
request response times (and then derives aggregate val-
ues) and one that predicts aggregate values directly from
workload characteristics. After being trained on the de-
vice in question, both provide accurate black-box mod-
els 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

  

Source: Ailamaki, Anastassia - School of Computer Science, Carnegie Mellon University

 

Collections: Computer Technologies and Information Sciences