Adaptive and Dynamic Adjustment of Fault Detection Cycles in Cloud Computing

Document Type

Article

Publication Date

1-1-2021

Abstract

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.

Identifier

85087851911 (Scopus)

Publication Title

IEEE Transactions on Industrial Informatics

External Full Text Location

https://doi.org/10.1109/TII.2019.2922681

e-ISSN

19410050

ISSN

15513203

First Page

20

Last Page

30

Issue

1

Volume

17

Grant

NGII20160207

Fund Ref

National Natural Science Foundation of China

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