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

Peiyun Zhang, Sheng Shu, Mengchu Zhou

Research output: Contribution to journalArticlepeer-review

184 Scopus citations


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.

Original languageEnglish (US)
Article number8283971
Pages (from-to)445-456
Number of pages12
JournalIEEE/CAA Journal of Automatica Sinica
Issue number2
StatePublished - Mar 2018

All Science Journal Classification (ASJC) codes

  • Control and Optimization
  • Artificial Intelligence
  • Information Systems
  • Control and Systems Engineering


  • Cloud computing
  • Fault detection
  • Grid
  • Support vector machine (SVM)


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