%0 Conference Proceedings
%T Storage Device Performance Prediction with Selective Bagging Classification and Regression Tree
%+ University of Science and Technology of China
%+ Wayne State University [Detroit]
%A Zhang, Lei
%A Liu, Guiquan
%A Zhang, Xuechen
%A Jiang, Song
%A Chen, Enhong
%< avec comité de lecture
%( Lecture Notes in Computer Science
%B IFIP International Conference on Network and Parallel Computing (NPC)
%C Zhengzhou, China
%Y Chen Ding; Zhiyuan Shao; Ran Zheng
%I Springer
%3 Network and Parallel Computing
%V LNCS-6289
%P 121-133
%8 2010-09-13
%D 2010
%R 10.1007/978-3-642-15672-4_11
%K Performance prediction
%K Storage device modeling
%K CART
%K Ensemble learning
%K Bagging
%Z Computer Science [cs]/Digital Libraries [cs.DL]Conference papers
%X 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.
%G English
%2 https://inria.hal.science/hal-01054984/document
%2 https://inria.hal.science/hal-01054984/file/llncs.pdf
%L hal-01054984
%U https://inria.hal.science/hal-01054984
%~ IFIP-LNCS
%~ IFIP
%~ IFIP-LNCS-6289
%~ IFIP-NPC
%~ IFIP-2010