Open AccessEnd-to-end diagnosis of cloud systems against intermittent faults
Author(s) -
Chao Wang,
Zhongchuan Fu,
Yanyan Huo
Publication year - 2021
Publication title -
computer science and information systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.244
H-Index - 24
eISSN - 2406-1018
pISSN - 1820-0214
DOI - 10.2298/csis200620040w
Subject(s) - computer science , cloud computing , latency (audio) , artificial neural network , software , end to end principle , fault (geology) , real time computing , distributed computing , artificial intelligence , operating system , telecommunications , seismology , geology
The diagnosis of intermittent faults is challenging because of their random manifestation due to intricate mechanisms. Conventional diagnosis methods are no longer effective for these faults, especially for hierachical environment, such as cloud computing. This paper proposes a fault diagnosis method that can effectively identify and locate intermittent faults originating from (but not limited to) processors in the cloud computing environment. The method is end-to-end in that it does not rely on artificial feature extraction for applied scenarios, making it more generalizable than conventional neural network-based methods. It can be implemented with no additional fault detection mechanisms, and is realized by software with almost zero hardware cost. The proposed method shows a higher fault diagnosis accuracy than BP network, reaching 97.98% with low latency.