An online fault detection model and strategies based on SVM-grid in clouds

Document Type

Article

Publication Date

3-1-2018

Abstract

Online fault detection is one of the key technologies to improve the performance of cloud systems. The current data of cloud systems is to be monitored, collected and used to reflect their state. Its use can potentially help cloud managers take some timely measures before fault occurrence in clouds. Because of the complex structure and dynamic change characteristics of the clouds, existing fault detection methods suffer from the problems of low efficiency and low accuracy. In order to solve them, this work proposes an online detection model based on asystematic parameter-search method called SVM U+002D Grid, whose construction is based on a support vector machine U+0028 SVM U+0029. SVM U+002D Grid is used to optimize parameters in SVM. Proper attributes of a cloud system U+02BC s running data are selected by using Pearson correlation and principal component analysis for the model. Strategies of predicting cloud faults and updating fault sample databases are proposed to optimize the model and improve its performance. In comparison with some representative existing methods, the proposed model can achieve more efficient and accurate fault detection for cloud systems.

Identifier

85041902926 (Scopus)

Publication Title

IEEE Caa Journal of Automatica Sinica

External Full Text Location

https://doi.org/10.1109/JAS.2017.7510817

e-ISSN

23299274

ISSN

23299266

First Page

445

Last Page

456

Issue

2

Volume

5

Grant

NGII20160207

Fund Ref

National Natural Science Foundation of China

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