A Novel Adaptive Failure Detector for Distributed Systems

Dong Tian, Kaigui Wu, Xueming Li
{"title":"A Novel Adaptive Failure Detector for Distributed Systems","authors":"Dong Tian, Kaigui Wu, Xueming Li","doi":"10.1109/NAS.2008.37","DOIUrl":null,"url":null,"abstract":"Combining adaptive heartbeat mechanism with fuzzy grey prediction algorithm, a novel implementation of failure detector is presented. The main parts of the implementation are adaptive grey prediction layer and adaptive fuzzy rule-based classification layer. The former layer employs a GM(1,1) unified-dimensional new message model, only needs a small volume of sample data, to predict heartbeat arrival time dynamically. Then, the predict value and the message loss rate in specific period are act as input variations for the latter layer to decide failure/non-failure. Furthermore, algorithms of how to predict arrival time and how to construct adaptive fuzzy rule-based classification system are presented. Experimental results validate the availability of our failure detector in detail.","PeriodicalId":153238,"journal":{"name":"2008 International Conference on Networking, Architecture, and Storage","volume":"21 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2008-06-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"10","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2008 International Conference on Networking, Architecture, and Storage","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/NAS.2008.37","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 10

Abstract

Combining adaptive heartbeat mechanism with fuzzy grey prediction algorithm, a novel implementation of failure detector is presented. The main parts of the implementation are adaptive grey prediction layer and adaptive fuzzy rule-based classification layer. The former layer employs a GM(1,1) unified-dimensional new message model, only needs a small volume of sample data, to predict heartbeat arrival time dynamically. Then, the predict value and the message loss rate in specific period are act as input variations for the latter layer to decide failure/non-failure. Furthermore, algorithms of how to predict arrival time and how to construct adaptive fuzzy rule-based classification system are presented. Experimental results validate the availability of our failure detector in detail.
一种新的分布式系统自适应故障检测器
将自适应心跳机制与模糊灰色预测算法相结合,提出了一种故障检测器的实现方法。实现的主要部分是自适应灰色预测层和自适应模糊规则分类层。前一层采用GM(1,1)统一维新消息模型,仅需少量样本数据,即可动态预测心跳到达时间。然后,将特定时间段内的预测值和消息损失率作为输入变量,供后一层判断是否失效。在此基础上,提出了预测到达时间的算法和构建自适应模糊规则分类系统的算法。实验结果详细验证了故障检测器的有效性。
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:481959085
Book学术官方微信