一个自我监控分析和报告技术数据集,包含147,496个硬盘的数据
Shuting Wei1, Hongzhang Yang2, Zhengguang Chen3
1Tianjin University of Technology, Tianjin, China.
Scientific data
|July 2, 2025
概括
本研究介绍了SMART-Z,这是一个全面的硬盘故障预测数据集. 比现有数据集,SMART-Z提供了更丰富的数据属性和显著减少空白数据,有助于开发强大的预测模型.
科学领域:
- 计算机科学 计算机科学
- 数据科学数据科学数据科学
- 可靠性工程可靠性工程
背景情况:
- 硬盘驱动器 (HDD) 的故障在大型数据中心中构成重大挑战.
- 准确的HDD故障预测对于保持系统稳定性和最大限度地减少数据丢失至关重要.
- 现有的数据集往往缺乏全面的属性和数据完整性,以有效地预测故障.
研究的目的:
- 介绍SMART-Z,一种用于硬盘故障预测的新而广泛的数据集.
- 为研究人员提供更丰富的数据集,包括关键值,设备IP和业务场景.
- 促进开发更准确,更可靠的HDD故障预测模型.
主要方法:
- 从中国的一个大型分布式视频数据中心收集了147,496块硬盘自我监控,分析和报告技术 (SMART) 数据.
- 通过排除高峰时段,多维监控和智能保险丝策略,确保数据完整性并最大限度地减少业务干扰.
- 包括65个硬盘模型,包括712个故障磁盘和许多健康的磁盘.
主要成果:
- SMART-Z数据集显示了5.3%的低空白数据率,明显超过了2022年Backblaze ST4000DM000数据集的14.78%空白数据率.
- 数据集包括有价值的属性,如关键值,最差值,设备IP,业务场景和驱动器字母名称.
- 这些数据涵盖2017年3月至2018年2月的时间,使时间序列分析和基于场景的设备分配统计数据成为可能.
结论:
- SMART-Z是一个有价值的,高质量的数据集,用于推进硬盘故障预测研究.
- 数据集的全面属性和卓越的数据完整性使得更复杂的时间序列和基于场景的分析成为可能.
- 在企业环境中,SMART-Z支持开发更有效的HDD故障预测模型.
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