Storage device performance prediction with selective bagging classification and regression tree

Authors:
Lei Zhang;Guiquan Liu;Xuechen Zhang;Song Jiang;Enhong Chen
Affiliations:
University of Science and Technology of China;University of Science and Technology of China;Wayne State University;Wayne State University;University of Science and Technology of China
Venue:
NPC'10 Proceedings of the 2010 IFIP international conference on Network and parallel computing
Year:
2010

Citing 16
Cited 1

An introduction to disk drive modeling

Computer
Scheduling algorithms for modern disk drives

SIGMETRICS '94 Proceedings of the 1994 ACM SIGMETRICS conference on Measurement and modeling of computer systems
The TickerTAIP parallel RAID architecture

ACM Transactions on Computer Systems (TOCS)
The HP AutoRAID hierarchical storage system

ACM Transactions on Computer Systems (TOCS) - Special issue on operating system principles
Bagging predictors

Machine Learning
Modeling and optimizing I/O throughput of multiple disks on a bus

SIGMETRICS '99 Proceedings of the 1999 ACM SIGMETRICS international conference on Measurement and modeling of computer systems
LIRS: an efficient low inter-reference recency set replacement policy to improve buffer cache performance

SIGMETRICS '02 Proceedings of the 2002 ACM SIGMETRICS international conference on Measurement and modeling of computer systems
Ensembling neural networks: many could be better than all

Artificial Intelligence
An Empirical Comparison of Voting Classification Algorithms: Bagging, Boosting, and Variants

Machine Learning
Option Decision Trees with Majority Votes

ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
Timing-Accurate Storage Emulation

FAST '02 Proceedings of the Conference on File and Storage Technologies
Clustering ensembles of neural network models

Neural Networks
A Modular, Analytical Throughput Model for Modern Disk Arrays

MASCOTS '01 Proceedings of the Ninth International Symposium in Modeling, Analysis and Simulation of Computer and Telecommunication Systems
Storage Device Performance Prediction with CART Models

MASCOTS '04 Proceedings of the The IEEE Computer Society's 12th Annual International Symposium on Modeling, Analysis, and Simulation of Computer and Telecommunications Systems
Pruning in ordered bagging ensembles

ICML '06 Proceedings of the 23rd international conference on Machine learning
Modeling the relative fitness of storage

Proceedings of the 2007 ACM SIGMETRICS international conference on Measurement and modeling of computer systems

Fourier-assisted machine learning of hard disk drive access time models

PDSW '13 Proceedings of the 8th Parallel Data Storage Workshop

Quantified Score

Hi-index	0.00

Visualization

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.