TY - JOUR
T1 - Adaptive and Dynamic Adjustment of Fault Detection Cycles in Cloud Computing
AU - Zhang, Peiyun
AU - Shu, Sheng
AU - Zhou, Mengchu
N1 - Funding Information:
Manuscript received February 5, 2019; accepted March 30, 2019. Date of publication June 13, 2019; date of current version October 23, 2020. This work was supported in part by the National Natural Science Foundation of China under Grant 61872006, Grant 61472005, and Grant 61201252, and in part by the China Education and Research Network (CERNET) Innovation Project under Grant NGII20160207. Paper no. TII-19-0402. (Corresponding author: MengChu Zhou.) P. Y. Zhang and S. Shu are with the School of Computer Science, Anhui Normal University, Wuhu 241003, China (e-mail:,zpyanu@ahnu.edu.cn; sh.shu@foxmail.com).
PY - 2021/1
Y1 - 2021/1
N2 - 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.
AB - 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.
KW - Abnormality
KW - adaptive adjustment
KW - cloud environment
KW - dynamic cycle
KW - fault detection
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U2 - 10.1109/TII.2019.2922681
DO - 10.1109/TII.2019.2922681
M3 - Article
AN - SCOPUS:85087851911
SN - 1551-3203
VL - 17
SP - 20
EP - 30
JO - IEEE Transactions on Industrial Informatics
JF - IEEE Transactions on Industrial Informatics
IS - 1
M1 - 8736274
ER -