Full Text:   <967>

Summary:  <259>

CLC number: TN333

On-line Access: 2023-07-24

Received: 2022-10-19

Revision Accepted: 2023-07-24

Crosschecked: 2023-06-13

Cited: 0

Clicked: 674

Citations:  Bibtex RefMan EndNote GB/T7714


Yunchuan GUAN




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Frontiers of Information Technology & Electronic Engineering  2023 Vol.24 No.7 P.964-979


A disk failure prediction model for multiple issues

Author(s):  Yunchuan GUAN, Yu LIU, Ke ZHOU, Qiang LI, Tuanjie WANG, Hui LI

Affiliation(s):  Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology, Wuhan 430074, China; more

Corresponding email(s):   hustgyc@hust.edu.cn, liu_yu@hust.edu.cn

Key Words:  Storage system reliability, Disk failure prediction, Self-monitoring analysis and reporting technology (SMART), Machine learning

Yunchuan GUAN, Yu LIU, Ke ZHOU, Qiang LI, Tuanjie WANG, Hui LI. A disk failure prediction model for multiple issues[J]. Frontiers of Information Technology & Electronic Engineering, 2023, 24(7): 964-979.

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author="Yunchuan GUAN, Yu LIU, Ke ZHOU, Qiang LI, Tuanjie WANG, Hui LI",
journal="Frontiers of Information Technology & Electronic Engineering",
publisher="Zhejiang University Press & Springer",

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%T A disk failure prediction model for multiple issues
%A Yunchuan GUAN
%A Qiang LI
%A Tuanjie WANG
%A Hui LI
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%I Zhejiang University Press & Springer
%DOI 10.1631/FITEE.2200488

T1 - A disk failure prediction model for multiple issues
A1 - Yunchuan GUAN
A1 - Yu LIU
A1 - Ke ZHOU
A1 - Qiang LI
A1 - Tuanjie WANG
A1 - Hui LI
J0 - Frontiers of Information Technology & Electronic Engineering
VL - 24
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SP - 964
EP - 979
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Y1 - 2023
PB - Zhejiang University Press & Springer
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DOI - 10.1631/FITEE.2200488

disk failure prediction methods have been useful in handing a single issue, e.g., heterogeneous disks, model aging, and minority samples. However, because these issues often exist simultaneously, prediction models that can handle only one will result in prediction bias in reality. Existing disk failure prediction methods simply fuse various models, lacking discussion of training data preparation and learning patterns when facing multiple issues, although the solutions to different issues often conflict with each other. As a result, we first explore the training data preparation for multiple issues via a data partitioning pattern, i.e., our proposed multi-property data partitioning (MDP). Then, we consider learning with the partitioned data for multiple issues as learning multiple tasks, and introduce the model-agnostic meta-learning (MAML) framework to achieve the learning. Based on these improvements, we propose a novel disk failure prediction model named MDP-MAML. MDP addresses the challenges of uneven partitioning and difficulty in partitioning by time, and MAML addresses the challenge of learning with multiple domains and minor samples for multiple issues. In addition, MDP-MAML can assimilate emerging issues for learning and prediction. On the datasets reported by two real-world data centers, compared to state-of-the-art methods, MDP-MAML can improve the area under the curve (AUC) and false detection rate (FDR) from 0.85 to 0.89 and from 0.85 to 0.91, respectively, while reducing the false alarm rate (FAR) from 4.88% to 2.85%.




Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article


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