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.
End-to-end diagnosis of cloud systems against intermittent faults