公开(公告)号：US20190205193A1

公开(公告)日：2019-07-04

申请号：US16135480

申请日：2018-09-19

Applicant: Huazhong University of Science and Technology

Inventor： Song WU ,  Zhuang XIONG ,  Hai JIN

IPC: G06F11/00 ,  G06F11/22 ,  G06F11/34 ,  G06F11/30 ,  G06N99/00 ,  G06K9/62

CPC classification number: G06F11/008 ,  G06F11/2221 ,  G06F11/2268 ,  G06F11/2273 ,  G06F11/3034 ,  G06F11/3485 ,  G06F2201/81 ,  G06K9/6269 ,  G06N20/00

Abstract: An S.M.A.R.T. threshold optimization method used for disk failure detection includes the steps of: analyzing S.M.A.R.T. attributes based on correlation between S.M.A.R.T. attribute information about plural failed and non-failed disks and failure information and sieving out weakly correlated attributes and/or strongly correlated attributes; and setting threshold intervals, multivariate thresholds and/or native thresholds corresponding to the S.M.A.R.T. attributes based on distribution patterns of the strongly or weakly correlated attributes. As compared to reactive fault tolerance, the disclosed method has no negative effects on reading and writing performance of disks and performance of storage systems as a whole. As compared to the known methods that use native disk S.M.A.R.T. thresholds, the disclosed method significantly improves disk failure detection rate with a low false alarm rate. As compared to disk failure forecast based on machine learning algorithm, the disclosed method has good interpretability and allows easy adjustment of its forecast performance.