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Storage Device Performance Prediction with Selective Bagging Classification and Regression Tree.

Authors :
Zhang, Lei
Liu, Guiquan
Zhang, Xuechen
Jiang, Song
Chen, Enhong
Source :
Network & Parallel Computing (9783642156717); 2010, p121-133, 13p
Publication Year :
2010

Abstract

Storage device performance prediction is a key element of self-managed storage systems and application planning tasks, such as data assignment and configuration. Based on bagging ensemble, we proposed an algorithm named selective bagging classification and regression tree (SBCART) to model storage device performance. In addition, we consider the caching effect as a feature in workload characterization. Experiments indicate that caching effect added in feature vector can substantially improve prediction accuracy and SBCART is more precise and more stable compared to CART. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISBNs :
9783642156717
Database :
Complementary Index
Journal :
Network & Parallel Computing (9783642156717)
Publication Type :
Book
Accession number :
76760993
Full Text :
https://doi.org/10.1007/978-3-642-15672-4_11