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A Proactive Failure Tolerant Mechanism for SSDs Storage Systems based on Unsupervised Learning
基于无监督学习的SSD存储系统主动容错机制
教師なし学習に基づくSSDSストレージシステムのためのプロアクティブ故障耐性メカニズム
무 감독 학습 기반 SSD 저장 시스템 능 동적 오류 처리 메커니즘
Mecanismo activo de tolerancia a fallos del sistema de almacenamiento SSD basado en el aprendizaje no supervisado
Mécanisme actif de tolérance aux pannes du système de stockage SSD basé sur un apprentissage non supervisé
система хранения SSD, основанная на обучении без надзора
Hao Zhou 周浩 ¹, Zhiheng Niu ², Gang Wang 王刚 ², XiaoGuang Liu 刘晓光 ², Dongshi Liu ³, Bingnan Kang ³, Hu Zheng ³, Yong Zhang ³
¹ College of Cyber Science TJ Key Lab of NDST, Nankai University, Tianjin, China
中国 天津 南开大学网络空间安全学院 天津市网络与数据安全技术重点实验室
² College of Computer Science TJ Key Lab of NDST, Nankai University, Tianjin, China
中国 天津 南开大学计算机学院 天津市网络与数据安全技术重点实验室
³ Huawei
华为
2021 IEEE/ACM 29th International Symposium on Quality of Service (IWQOS), 26 August 2021
Abstract

As a proactive failure tolerant mechanism in large scale cloud storage systems, drive failure prediction can be used to protect data by early warning before real failures of drives, and therefore improve system dependability and cloud storage service quality. At present, solid state drives (SSDs) are generally widely used in cloud storage systems due to their high performance. SSD failures seriously affect the dependability of the system and the quality of service. Existing proactive failure tolerant mechanisms for storage systems are basically aimed at HDD failure detection and use classification technology (Supervised learning), which relies on enough failure data to establish a classification model.

However, the low failure rate of SSDs leads to a serious imbalance in the ratio of positive and negative samples, which brings a big challenge for establishing a proactive failure tolerance mechanism for SSDs storage systems by using classification technology.In this paper, we propose a proactive failure tolerance mechanism for SSDs storage systems based on unsupervised technology. It only uses data of normal SSDs to train the failure prediction model, which means that our method is not limited by the imbalance in SSDs data.

At the core of our method is the idea to use VAE-LSTM to learn the pattern of normal SSDs, in which case faulty SSDs can be alerted when their patterns are very different from normal ones. Our method can provide early warning of failures, thereby effectively protecting data and improving the quality of cloud storage service. We also propose a drive failure cause location mechanism, which can help operators analyze the modes of failure by providing guiding suggestions. In order to evaluate the effectiveness of our method, we use cross-validation and online testing methods on SSDs data from a technology company. The results show that the FDR and FAR of our method outperform the baselines by 17.25% and 2.39% on average.
2021 IEEE/ACM 29th International Symposium on Quality of Service (IWQOS)_1
2021 IEEE/ACM 29th International Symposium on Quality of Service (IWQOS)_2
2021 IEEE/ACM 29th International Symposium on Quality of Service (IWQOS)_3
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