Fault Diagnosis Analysis in Large-Scale Computing Environments

摘要：

This paper issues the problem of fault diagnosis in high computing system. In order to solve this problem, i.e., correctly and efficiently detecting the anomaly nodes during the system operation, which is very similar to the principle of pattern recognition research work, thus we try to use some pattern recognition methods to analysis and solve fault diagnosis problem in this paper. And also we do some experiment and compare the results and finally get some useful conclusion to show that Kernel Eigenface and Kernel Fisherface methods achieve lower error rates than the ICA and PCA approaches in anomaly nodes detection.

关键词： fault diagnosis pattern recognition feature extraciton PCA KPCA ICA FDA KFDA

作者: Yan Xue Xuefang Zhu

作者单位: Dept. of Information Management in Nanjing University Nanjing, Jiangsu

会议类型: 国际会议

会议名称: 2010年IEEE多媒体信息网络与安全国际会议

会议地点: 南京

会议语种:英文

页码: 551-554

在线出版日期: 2010-11-01（万方平台首次上网日期，不代表论文的发表时间）