changes of disks, performance indicator fluctuations, and disk logs, ensuring more accurate prediction results.įor SSDs, the interfaces provide SCSI log page information that records the current disk status and performance indicators, such as the grown defect list, non-medium error, and read/write/verify uncorrected errors. eService uses intelligence technologies to dynamically analyze S.M.A.R.T.
However, it is difficult to ensure the accuracy of prediction results. data can indicate the running status of the disks and help predict risky disks in a certain range. eService uses intelligent algorithms to predict SSD risks to detect failed SSDs and replace risky SSDs in advance, preventing faults and improving system reliability.įigure 1-1 Working principles of disk health prediction
In addition, the number of read and write requests delivered by service varies every day, which further complicates disk service life prediction.ĮService collects the Self-Monitoring, Analysis and Reporting Technology (S.M.A.R.T.) information and I/O link information of SSDs, as well as reliability indicators of SSDs, and enters such information to hundreds of disk failure prediction models, implementing accurate SSD service life prediction. SSDs are electronic components and their service life prediction indicators are limited. Therefore, disk service life is the most concerned topic for many users. When two disks fail, the storage system stops providing services to ensure data reliability.ĭisks are the largest consumables in a storage system. For example, RAID 5 allows only one disk to fail. Although various redundancy technologies are used in storage systems, they allow the failure of a limited number of disks while ensuring service running.
0 kommentar(er)
