A Vulnerability Factor for ECC-protected Memory

Luc Jaulmes, Miquel Moretó, M. Valero, Marc Casas
{"title":"A Vulnerability Factor for ECC-protected Memory","authors":"Luc Jaulmes, Miquel Moretó, M. Valero, Marc Casas","doi":"10.1109/IOLTS.2019.8854397","DOIUrl":null,"url":null,"abstract":"Fault injection studies and vulnerability analyses have been used to estimate the reliability of data structures in memory. We survey these metrics and look at their adequacy to describe the data stored in ECC-protected memory. We also introduce FEA, a new metric improving on the memory derating factor by ignoring a class of false errors. We measure all metrics using simulations and compare them to the outcomes of injecting errors in real runs. This in-depth study reveals that FEA provides more accurate results than any state-of-the-art vulnerability metric. Furthermore, FEA gives an upper bound on the failure probability due to an error in memory, making this metric a tool of choice to quantify memory vulnerability. Finally, we show that ignoring these false errors reduces the failure rate on average by 12.75% and up to over 45%.","PeriodicalId":383056,"journal":{"name":"2019 IEEE 25th International Symposium on On-Line Testing and Robust System Design (IOLTS)","volume":"60 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2018-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"4","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2019 IEEE 25th International Symposium on On-Line Testing and Robust System Design (IOLTS)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/IOLTS.2019.8854397","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 4

Abstract

Fault injection studies and vulnerability analyses have been used to estimate the reliability of data structures in memory. We survey these metrics and look at their adequacy to describe the data stored in ECC-protected memory. We also introduce FEA, a new metric improving on the memory derating factor by ignoring a class of false errors. We measure all metrics using simulations and compare them to the outcomes of injecting errors in real runs. This in-depth study reveals that FEA provides more accurate results than any state-of-the-art vulnerability metric. Furthermore, FEA gives an upper bound on the failure probability due to an error in memory, making this metric a tool of choice to quantify memory vulnerability. Finally, we show that ignoring these false errors reduces the failure rate on average by 12.75% and up to over 45%.
ecc保护内存的漏洞因子
故障注入研究和漏洞分析已被用于估计内存中数据结构的可靠性。我们调查了这些指标,看看它们是否足以描述存储在ecc保护内存中的数据。我们还介绍了FEA,这是一种通过忽略一类虚假错误来改进内存降额因子的新指标。我们使用模拟测量所有指标,并将其与实际运行中注入错误的结果进行比较。这项深入的研究表明,有限元分析法提供了比任何最先进的漏洞度量更准确的结果。此外,有限元分析给出了由于内存错误而导致的故障概率的上限,使该指标成为量化内存漏洞的首选工具。最后,我们表明忽略这些虚假错误平均降低了12.75%的失败率,最高可达45%以上。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
自引率
0.00%
发文量
0
×
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
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
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 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:604180095
Book学术官方微信