Adaptive and Dynamic Adjustment of Fault Detection Cycles in Cloud Computing

Peiyun Zhang, Sheng Shu, Mengchu Zhou

Research output: Contribution to journalArticlepeer-review

20 Scopus citations


In past decades, we witnessed many applications and fast development of cloud computing technologies. Cloud faults are encountered in a cloud computing environment. They badly impact users and cause serious economic losses in business. As a vital technology, fault detection can guarantee a high reliability cloud environment. However, fault detection with a fixed detection cycle has defects and shortcomings. On the one hand, for the service with good performance, if a small cycle is set, it may need a lot of system overhead due to unnecessary over detection; on the other hand, for the service with poor performance, if a large cycle is set, it may result in the omission of faults which should be detected. To address these issues, in this paper, a fault detection model is proposed to improve the detection accuracy based on support vector machine and a decision tree. For abnormal samples, their abnormality is calculated by using the model. We design algorithms to adaptively and dynamically adjust cycles for fault detection. The cycle is shortened if a system experiences many faults, thus increasing fault detection success rate; it is lengthened if the system runs without any problem, thereby reducing much computational overhead. Experimental results show that the proposed method outperforms two classical methods, i.e., one based on self-organizing competitive neutral network and the other based on a probabilistic neural network.

Original languageEnglish (US)
Article number8736274
Pages (from-to)20-30
Number of pages11
JournalIEEE Transactions on Industrial Informatics
Issue number1
StatePublished - Jan 2021

All Science Journal Classification (ASJC) codes

  • Control and Systems Engineering
  • Information Systems
  • Computer Science Applications
  • Electrical and Electronic Engineering


  • Abnormality
  • adaptive adjustment
  • cloud environment
  • dynamic cycle
  • fault detection


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