Boureau, Y.L., Ach, F., Ecun, Y., et al. (2010) Learning Mid-Level Features for Recognition. IEEE Conference on Computer Vision and Patten Recognition, San Francisco, 13-18 June 2010, 2559-2566.

has been cited by the following article:

• TITLE: A Cloud Computing Fault Detection Method Based on Deep Learning

  AUTHORS: Weipeng Gao, Youchan Zhu

  KEYWORDS: Fault Detection, Cloud Computing, Auto-Encoder, Sparse Denoising, Deep Learning

  JOURNAL NAME: Journal of Computer and Communications, Vol.5 No.12, October 13, 2017

  ABSTRACT: In the cloud computing, in order to provide reliable and continuous service, the need for accurate and timely fault detection is necessary. However, cloud failure data, especially cloud fault feature data acquisition is difficult and the amount of data is too small, with large data training methods to solve a certain degree of difficulty. Therefore, a fault detection method based on depth learning is proposed. An auto-encoder with sparse denoising is used to construct a parallel structure network. It can automatically learn and extract the fault data characteristics and realize fault detection through deep learning. The experiment shows that this method can detect the cloud computing abnormality and determine the fault more effectively and accurately than the traditional method in the case of the small amount of cloud fault feature data.