基于中心性度量的云故障定位

R. NarayanaaS, M. Sivaranjan, S. LekshmiR
{"title":"基于中心性度量的云故障定位","authors":"R. NarayanaaS, M. Sivaranjan, S. LekshmiR","doi":"10.1109/STC55697.2022.00033","DOIUrl":null,"url":null,"abstract":"Fault localization is an imperative method in fault tolerance in a distributed environment that designs a blueprint for continuing the ongoing process even when one or many modules are non-functional. Visualizing a distributed environment as a graph, whose nodes represent faults (fault graph), allows us to introduce probabilistic weights to both edges and nodes that cause the faults. With multiple modules like databases, run-time cloud, etc. making up a distributed environment and extensively, a cloud environment, we aim to address the problem of optimally and accurately performing fault localization in a distributed environment by modifying the Graph optimization approach to localization and centrality, specific to fault graphs.","PeriodicalId":170123,"journal":{"name":"2022 IEEE 29th Annual Software Technology Conference (STC)","volume":"25 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2021-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Fault Localization in Cloud using Centrality Measures\",\"authors\":\"R. NarayanaaS, M. Sivaranjan, S. LekshmiR\",\"doi\":\"10.1109/STC55697.2022.00033\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Fault localization is an imperative method in fault tolerance in a distributed environment that designs a blueprint for continuing the ongoing process even when one or many modules are non-functional. Visualizing a distributed environment as a graph, whose nodes represent faults (fault graph), allows us to introduce probabilistic weights to both edges and nodes that cause the faults. With multiple modules like databases, run-time cloud, etc. making up a distributed environment and extensively, a cloud environment, we aim to address the problem of optimally and accurately performing fault localization in a distributed environment by modifying the Graph optimization approach to localization and centrality, specific to fault graphs.\",\"PeriodicalId\":170123,\"journal\":{\"name\":\"2022 IEEE 29th Annual Software Technology Conference (STC)\",\"volume\":\"25 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2021-09-23\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2022 IEEE 29th Annual Software Technology Conference (STC)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/STC55697.2022.00033\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2022 IEEE 29th Annual Software Technology Conference (STC)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/STC55697.2022.00033","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

摘要

故障定位是分布式环境容错中的一种必要方法,分布式环境可以设计蓝图,以便在一个或多个模块失效时继续执行正在进行的流程。将分布式环境可视化为一个图,其节点表示故障(故障图),允许我们向导致故障的边和节点引入概率权重。数据库、运行时云等多个模块组成了一个分布式环境和一个广泛的云环境,我们的目标是通过修改图优化方法来定位和中心性,具体到故障图,解决在分布式环境中最优、准确地执行故障定位的问题。
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
Fault Localization in Cloud using Centrality Measures
Fault localization is an imperative method in fault tolerance in a distributed environment that designs a blueprint for continuing the ongoing process even when one or many modules are non-functional. Visualizing a distributed environment as a graph, whose nodes represent faults (fault graph), allows us to introduce probabilistic weights to both edges and nodes that cause the faults. With multiple modules like databases, run-time cloud, etc. making up a distributed environment and extensively, a cloud environment, we aim to address the problem of optimally and accurately performing fault localization in a distributed environment by modifying the Graph optimization approach to localization and centrality, specific to fault graphs.
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
On the Detection of Performance Regression Introducing Code Changes: Experience from the Git Project AI Assurance for the Public – Trust but Verify, Continuously Bayesian Approach for Regression Testing (BART) using Test Suite Prioritization Model-Agnostic Scoring Methods for Artificial Intelligence Assurance Project Features That Make Machine-Learning Based Fault Proneness Analysis Successful
×
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