A disk failure detection method using multi-model prediction

Abstract

The invention discloses a disk fault detection method using multi-model prediction, which extracts multiple characteristics of disk SMART indexes through a sequential data processing means, and establishes a classification model to predict disk state. Step 1, data input: acquiring a data set composed of monitoring data of a plurality of disks in a period of time; step 2, SMART screening: adoptingmutation point detection mode to select SMART index; step 3, feature engineering: using SMART index as the input of the user-defined feature extraction module to extract the features of the SMART index, then extracting the corresponding parameter configuration, and transmitting the parameter configuration to the feature extraction module as a parameter, so as to extract the feature sets of the training set and the test set; step 4, data set balance: desampling the negative sample which occupies a large amount by adopting dimension reduction clustering; step 5, algorithm selection and modeling:on the basis of the step 4, training the classification model and testing whether the current disk belongs to the normal state or the fault state that needs to be replaced.

Description

technical field [0001] The invention relates to the field of data mining, in particular to a disk failure prediction algorithm. Background technique [0002] In recent years, with the development of emerging technologies such as cloud storage, mass data storage technology has developed faster and faster, and data centers have become increasingly large. In the downtime cost of the data center, the failure of network equipment is an important factor, and the disk, as the final storage place of data, is one of the most important network equipment and the most frequently failed equipment. As the importance of data continues to increase, the impact of accidents caused by disk-based storage device failures is increasing, and the cost of data recovery is getting higher and higher. [0003] Disk failures are generally divided into two types: predictable and unpredictable. Unpredictable faults, such as transient faults such as sudden chip failure, have a process such as motor beari...

AI Technical Summary

Problems solved by technology

However, the failure prediction method based on the threshold is too simple, the accuracy rate is low, and the disk failure cannot be predicted in advance

[0005] Existing fault detection methods based on machine learning algorithms do not fully extract the timing characteristics of SMART indicators

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