Ji Zhang, Huazhong University of Science and Technology and University of Amsterdam; Ping Huang, Huazhong University of Science and Technology and Temple University; Ke Zhou, Huazhong University of Science and Technology; Ming Xie, Tencent Inc.; Sebastian Schelter, University of Amsterdam
The reliability of a storage system is crucial in large data centers. Hard disks are widely used as primary storage devices in modern data centers, where disk failures constantly happen. Disk failures could lead to a serious system interrupt or even permanent data loss. Many hard disk failure detection approaches have been proposed to solve this problem. However, existing approaches are not generic models for heterogeneous disks in large data centers, e.g, most of the approaches only consider datasets consisting of disks from the same manufacturer (and often of the same disk models). Moreover, some approaches achieve high detection performance in most cases but can not deliver satisfactory results when the datasets of a relatively small amount of disks or have new datasets which have not been seen during training. In this paper, we propose a novel generic disk failure detection approach for heterogeneous disks that can not only deliver a better detective performance but also have good detective adaptability to the disks which have not appeared in training, even when dealing with imbalanced or a relatively small amount of disk datasets. We employ a Long Short-Term Memory (LSTM) based siamese network that can learn the dynamically changed long-term behavior of disk healthy statues. Moreover, this structure can generate a unified and efficient high dimensional disk state embeddings for failure detection of heterogeneous disks. Our evaluation results on two real-world data centers confirm that the proposed system is effective and outperforms several state-of-the-art approaches. Furthermore, we have successfully applied the proposed system to improve the reliability of a data center and exhibit practical long-term availability.
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