用量化磁盘退化特征表征磁盘故障:早期经验

Song Huang, Song Fu, Quan Zhang, Weisong Shi
{"title":"用量化磁盘退化特征表征磁盘故障:早期经验","authors":"Song Huang, Song Fu, Quan Zhang, Weisong Shi","doi":"10.1109/IISWC.2015.26","DOIUrl":null,"url":null,"abstract":"With the advent of cloud computing and online services, large enterprises rely heavily on their data centers to serve end users. Among different server components, hard disk drives are known to contribute significantly to server failures. Disk failures as well as their impact on the performance of storage systems and operating costs are becoming an increasingly important concern for data center designers and operators. However, there is very little understanding on the characteristics of disk failures in data centers. Effective disk failure management and data recovery also requires a deep understanding of the nature of disk failures. In this paper, we present a systematic approach to provide a holistic and insightful view of disk failures. We study a large-scale storage system from a production data center. We categorize disk failures based on their distinctive manifestations and properties. Then we characterize the degradation of disk errors to failures by deriving the degradation signatures for each failure category. The influence of disk health attributes on failure degradation is also quantified. We discuss leveraging the derived degradation signatures to forecast disk failures even in their early stages. To the best of our knowledge, this is the first work that shows how to discover the categories of disk failures and characterize their degradation processes on a production data center.","PeriodicalId":142698,"journal":{"name":"2015 IEEE International Symposium on Workload Characterization","volume":"8 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2015-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"29","resultStr":"{\"title\":\"Characterizing Disk Failures with Quantified Disk Degradation Signatures: An Early Experience\",\"authors\":\"Song Huang, Song Fu, Quan Zhang, Weisong Shi\",\"doi\":\"10.1109/IISWC.2015.26\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"With the advent of cloud computing and online services, large enterprises rely heavily on their data centers to serve end users. Among different server components, hard disk drives are known to contribute significantly to server failures. Disk failures as well as their impact on the performance of storage systems and operating costs are becoming an increasingly important concern for data center designers and operators. However, there is very little understanding on the characteristics of disk failures in data centers. Effective disk failure management and data recovery also requires a deep understanding of the nature of disk failures. In this paper, we present a systematic approach to provide a holistic and insightful view of disk failures. We study a large-scale storage system from a production data center. We categorize disk failures based on their distinctive manifestations and properties. Then we characterize the degradation of disk errors to failures by deriving the degradation signatures for each failure category. The influence of disk health attributes on failure degradation is also quantified. We discuss leveraging the derived degradation signatures to forecast disk failures even in their early stages. To the best of our knowledge, this is the first work that shows how to discover the categories of disk failures and characterize their degradation processes on a production data center.\",\"PeriodicalId\":142698,\"journal\":{\"name\":\"2015 IEEE International Symposium on Workload Characterization\",\"volume\":\"8 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2015-10-04\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"29\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2015 IEEE International Symposium on Workload Characterization\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/IISWC.2015.26\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2015 IEEE International Symposium on Workload Characterization","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/IISWC.2015.26","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 29

摘要

随着云计算和在线服务的出现,大型企业严重依赖其数据中心为最终用户提供服务。在不同的服务器组件中,硬盘驱动器是导致服务器故障的主要原因。磁盘故障及其对存储系统性能和运营成本的影响正成为数据中心设计人员和运营商日益关注的问题。然而,人们对数据中心中磁盘故障的特征了解甚少。有效的磁盘故障管理和数据恢复还需要深入了解磁盘故障的性质。在本文中,我们提出了一种系统的方法来提供磁盘故障的整体和有见地的观点。我们研究了一个生产数据中心的大规模存储系统。我们根据磁盘故障的独特表现和特性对其进行分类。然后,我们通过导出每个故障类别的退化特征来表征磁盘错误到故障的退化。还量化了磁盘健康属性对故障退化的影响。我们将讨论利用派生的退化特征来预测磁盘故障,即使是在故障的早期阶段。据我们所知,这是第一个展示如何在生产数据中心发现磁盘故障类别并描述其降级过程的工作。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
Characterizing Disk Failures with Quantified Disk Degradation Signatures: An Early Experience
With the advent of cloud computing and online services, large enterprises rely heavily on their data centers to serve end users. Among different server components, hard disk drives are known to contribute significantly to server failures. Disk failures as well as their impact on the performance of storage systems and operating costs are becoming an increasingly important concern for data center designers and operators. However, there is very little understanding on the characteristics of disk failures in data centers. Effective disk failure management and data recovery also requires a deep understanding of the nature of disk failures. In this paper, we present a systematic approach to provide a holistic and insightful view of disk failures. We study a large-scale storage system from a production data center. We categorize disk failures based on their distinctive manifestations and properties. Then we characterize the degradation of disk errors to failures by deriving the degradation signatures for each failure category. The influence of disk health attributes on failure degradation is also quantified. We discuss leveraging the derived degradation signatures to forecast disk failures even in their early stages. To the best of our knowledge, this is the first work that shows how to discover the categories of disk failures and characterize their degradation processes on a production data center.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
自引率
0.00%
发文量
0
期刊最新文献
Fast Computational GPU Design with GT-Pin On Power-Performance Characterization of Concurrent Throughput Kernels CRONO: A Benchmark Suite for Multithreaded Graph Algorithms Executing on Futuristic Multicores Exploring Parallel Programming Models for Heterogeneous Computing Systems Revealing Critical Loads and Hidden Data Locality in GPGPU Applications
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1