基于压缩因子的宽度学习系统的虚拟机性能预测

Wei Dong Zou; Yuan Qing Xia

doi:10.16383/j.aas.c190307

基于压缩因子的宽度学习系统的虚拟机性能预测

Translated title of the contribution: Virtual Machine Performance Prediction Using Broad Learning System Based on Compression Factor

Wei Dong Zou, Yuan Qing Xia^*

^*Corresponding author for this work

School of Automation

Beijing Institute of Technology

Research output: Contribution to journal › Article › peer-review

2 Citations (Scopus)

Abstract

In cloud service models which is based on IaaS, from the user＇s perspective, how to accurately predict performance of virtual machine is very important for making renting strategy of virtual machines between many physical servers. However, broad learning system (BLS) includes too many redundant feature nodes and enhancement nodes, resulting in decreased efficiency and accuracy of virtual machine performance prediction. Connecting compression factor to BLS, the paper builds intelligent prediction model of BLS based on compression factor (CF-BLS), and uses the model for predicting virtual machine performance.

Translated title of the contribution	Virtual Machine Performance Prediction Using Broad Learning System Based on Compression Factor
Original language	Chinese (Traditional)
Pages (from-to)	724-734
Number of pages	11
Journal	Zidonghua Xuebao/Acta Automatica Sinica
Volume	48
Issue number	3
DOIs	https://doi.org/10.16383/j.aas.c190307
Publication status	Published - Mar 2022

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title = "基于压缩因子的宽度学习系统的虚拟机性能预测",

abstract = "In cloud service models which is based on IaaS, from the user＇s perspective, how to accurately predict performance of virtual machine is very important for making renting strategy of virtual machines between many physical servers. However, broad learning system (BLS) includes too many redundant feature nodes and enhancement nodes, resulting in decreased efficiency and accuracy of virtual machine performance prediction. Connecting compression factor to BLS, the paper builds intelligent prediction model of BLS based on compression factor (CF-BLS), and uses the model for predicting virtual machine performance.",

keywords = "Broad learning system (BLS), Compression factor, Convergence rate of network, Generalization performance, Performance prediction of virtual machine",

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year = "2022",

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doi = "10.16383/j.aas.c190307",

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AU - Zou, Wei Dong

AU - Xia, Yuan Qing

PY - 2022/3

Y1 - 2022/3

N2 - In cloud service models which is based on IaaS, from the user＇s perspective, how to accurately predict performance of virtual machine is very important for making renting strategy of virtual machines between many physical servers. However, broad learning system (BLS) includes too many redundant feature nodes and enhancement nodes, resulting in decreased efficiency and accuracy of virtual machine performance prediction. Connecting compression factor to BLS, the paper builds intelligent prediction model of BLS based on compression factor (CF-BLS), and uses the model for predicting virtual machine performance.

AB - In cloud service models which is based on IaaS, from the user＇s perspective, how to accurately predict performance of virtual machine is very important for making renting strategy of virtual machines between many physical servers. However, broad learning system (BLS) includes too many redundant feature nodes and enhancement nodes, resulting in decreased efficiency and accuracy of virtual machine performance prediction. Connecting compression factor to BLS, the paper builds intelligent prediction model of BLS based on compression factor (CF-BLS), and uses the model for predicting virtual machine performance.

KW - Broad learning system (BLS)

KW - Compression factor

KW - Convergence rate of network

KW - Generalization performance

KW - Performance prediction of virtual machine

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JO - Zidonghua Xuebao/Acta Automatica Sinica

JF - Zidonghua Xuebao/Acta Automatica Sinica

IS - 3

ER -

基于压缩因子的宽度学习系统的虚拟机性能预测

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