Time sequence classification early warning method for storage device

Abstract

The invention discloses a time sequence classification early warning method for a storage device. The method comprises the steps of collecting storage device parameters in real time; cleaning data; performing ARIMA time sequence analysis; and performing logistic regression analysis and early warning mechanism output. Under the background of a big data environment, time sequence prediction analysisis performed by adopting an ARIMA model according to historical data and hard disk SMART information obtained by statistics; the correlation between a SMART eigenvalue and a fault rate of the storagedevice is analyzed; and an eigenvalue more suitable for a Logistic model is selected out to perform classification prediction. A machine learning method is adopted for predicting the fault rate of the storage device, so that the problems of classification singleness and low early warning intensity in final prediction of the storage device are solved, the defects of hysteresis, low accuracy, pooractual early warning effect and difficult application to the big data environment for a disk early warning mechanism in the prior art are overcome, the occurrence probability of each early warning intensity can be predicted, and an effective solution is provided for real-time operation maintenance and monitoring in a data center environment.

Description

technical field [0001] The present invention relates to the field of storage devices in a data center environment, including storage devices such as mechanical hard disks (Hard DiskDrive, HDD), solid state disks (Solid State Drive, SSD), hybrid hard disks (Hybrid Hard Disk, HHD) and disk arrays, More specifically, it relates to a comprehensive prediction implementation method for performance analysis and load analysis of a storage device early warning mechanism in a data center environment that integrates a time series prediction analysis model and a logistic regression classification model. Background technique [0002] In today's information age, a large amount of new information is generated every day. The total amount of global data is increasing at a rate of 50% per year. Nowadays, more and more data are stored in data centers. Storage is an indispensable part of data centers. Any data center data must eventually be placed on storage devices. As data The scale of the c...

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Problems solved by technology

[0014] The present invention proposes a storage device timing classification early warning method, which is used to improve the accuracy of storage device fault prediction on the premise of satisfying the low false alarm rate of prediction, and solve the hysteresis and accuracy of the storage device fault early warning mechanism in the prior art The rate is low, the actual early warning is not effective, and it is difficult to apply to the technical problems of the big data environment

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