Storage device fault prediction method and system

A storage device and fault prediction technology, applied in neural learning methods, detecting faulty computer hardware, using neural networks to detect faulty hardware, etc. To achieve the effect of reducing the false alarm rate and improving the fault detection rate

Active Publication Date: 2019-10-18
HUAZHONG UNIV OF SCI & TECH
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[0005] Aiming at the defects of the prior art, the purpose of the present invention is to solve the technical problems that the labels of the training samples of th

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  • Storage device fault prediction method and system

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[0038] In order to make the object, technical solution and advantages of the present invention clearer, the present invention will be further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described here are only used to explain the present invention, not to limit the present invention.

[0039] The present invention provides a storage device failure prediction method, the method includes the following steps:

[0040]S1. Receive the input minimum lead time LTMIN and maximum lead time LTMAX, and collect the SMART attribute data of N storage devices of the same storage device series at different time points in real time, ensuring that the collected data includes normal storage device data and faulty storage devices data;

[0041] S2. Randomly shuffle the order of all storage devices, and select the j-th storage device according to the order after the scramble;

[0042] S3. SMART att...

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Abstract

The invention discloses a storage device fault prediction method and system, and belongs to the technical field of computer storage. The method comprises the following steps: S1, acquiring SMART attribute data of N storage devices at different time points; S2, disordering the sequence of all the storage devices and selecting the j = 1 storage device; S3, using the SMART attribute data of the storage device at each time point as small-batch samples and input into a fault prediction model for training, and an output result is obtained; S4, dynamically adjusting the label and feedback weight of each sample according to the state of the time point tn of the storage device, the output result, the LTMIN and the LTMAX; s5, calculating the comprehensive loss Lossj of the batch; S6, selecting a next storage device, and repeating the steps S3 to S6; S5, calculating the total loss Lossfinal of all the storage devices in the period until all the storage devices are taken out; S7, judging whether Lossfinal is converged or not, if yes, obtaining a trained prediction model, entering the step S8, and otherwise, entering the step S2; and S8, inputting the current SMART attribute data of the to-be-predicted storage device into the trained prediction model to obtain a prediction result.

Description

technical field [0001] The invention belongs to the technical field of computer storage, and more particularly relates to a storage device failure prediction method and system. Background technique [0002] Due to the low price per storage capacity and mature technology, disks are widely deployed in data centers and are often used in cold data storage, long-term storage, backup storage and other applications. Once the disk fails, it will cause huge data loss if the data is not backed up, and if there is a backup, restoring the data will incur huge overhead, which will easily cause disk and network I / O bursts, affecting Use of Online Business. [0003] Usually, the data center collects the SMART data of the disk and I / O load statistical data, and uses the machine learning model to build a fault prediction model to evaluate the wear degree of the disk, speculate whether the disk will fail in the near future, and analyze the Potentially high-risk disks take fault handling mea...

Claims

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Application Information

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IPC IPC(8): G06F11/22G06N3/04G06N3/063G06N3/08
CPCG06F11/2263G06N3/08G06N3/063G06N3/048G06N3/044
Inventor 冯丹王芳谢燕文张鑫
Owner HUAZHONG UNIV OF SCI & TECH
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