基于检查点迁移技术的云容错模糊方法

Noshin Hagshenas, Musa Mojarad, Hassan Arfaeinia
{"title":"基于检查点迁移技术的云容错模糊方法","authors":"Noshin Hagshenas, Musa Mojarad, Hassan Arfaeinia","doi":"10.5815/ijisa.2022.03.02","DOIUrl":null,"url":null,"abstract":"Fault tolerance is one of the most important issues in cloud computing to provide reliable services. It is difficult to implement due to dynamic service infrastructures, complex configurations and different dependencies. Extensive research efforts have been made to implement fault tolerance in the cloud environment. Many studies focus only on fault detection and do not consider fault tolerance. For this reason, in this paper, in addition to recognizing the nature of the fault, a fuzzy logic-based approach is proposed to provide an appropriate response and increase the fault tolerance in the cloud environment. Checkpoint-based migration technique is used to increase fault tolerance. Using a checkpoint during migration can reduce time and processing costs and balance the load between virtual machines in the event of a fault. The simulation is performed according to the data center of Vietnam Telecommunications Company (VDC). The results of the proposed method in a period of 60 minutes show 98.03% fault detection accuracy, which is 4.5% and 4.1% superior to FLPT and PLBFT algorithms, respectively.","PeriodicalId":14067,"journal":{"name":"International Journal of Intelligent Systems and Applications in Engineering","volume":"188 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2022-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"A Fuzzy Approach to Fault Tolerant in Cloud using the Checkpoint Migration Technique\",\"authors\":\"Noshin Hagshenas, Musa Mojarad, Hassan Arfaeinia\",\"doi\":\"10.5815/ijisa.2022.03.02\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Fault tolerance is one of the most important issues in cloud computing to provide reliable services. It is difficult to implement due to dynamic service infrastructures, complex configurations and different dependencies. Extensive research efforts have been made to implement fault tolerance in the cloud environment. Many studies focus only on fault detection and do not consider fault tolerance. For this reason, in this paper, in addition to recognizing the nature of the fault, a fuzzy logic-based approach is proposed to provide an appropriate response and increase the fault tolerance in the cloud environment. Checkpoint-based migration technique is used to increase fault tolerance. Using a checkpoint during migration can reduce time and processing costs and balance the load between virtual machines in the event of a fault. The simulation is performed according to the data center of Vietnam Telecommunications Company (VDC). The results of the proposed method in a period of 60 minutes show 98.03% fault detection accuracy, which is 4.5% and 4.1% superior to FLPT and PLBFT algorithms, respectively.\",\"PeriodicalId\":14067,\"journal\":{\"name\":\"International Journal of Intelligent Systems and Applications in Engineering\",\"volume\":\"188 1\",\"pages\":\"\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2022-06-08\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"International Journal of Intelligent Systems and Applications in Engineering\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.5815/ijisa.2022.03.02\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"Computer Science\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Journal of Intelligent Systems and Applications in Engineering","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.5815/ijisa.2022.03.02","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"Computer Science","Score":null,"Total":0}
引用次数: 0

摘要

容错是云计算提供可靠服务的重要问题之一。由于动态的服务基础结构、复杂的配置和不同的依赖关系,实现起来比较困难。为了在云环境中实现容错,已经进行了大量的研究工作。许多研究只关注故障检测,而没有考虑容错问题。为此,本文在识别故障性质的基础上,提出了一种基于模糊逻辑的方法,在云环境中提供适当的响应,提高容错能力。采用基于检查点的迁移技术提高容错性。在迁移过程中使用检查点可以减少时间和处理成本,并在发生故障时平衡虚拟机之间的负载。仿真是根据越南电信公司(VDC)的数据中心进行的。结果表明,该方法在60分钟内的故障检测准确率为98.03%,分别比FLPT和PLBFT算法提高4.5%和4.1%。
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
A Fuzzy Approach to Fault Tolerant in Cloud using the Checkpoint Migration Technique
Fault tolerance is one of the most important issues in cloud computing to provide reliable services. It is difficult to implement due to dynamic service infrastructures, complex configurations and different dependencies. Extensive research efforts have been made to implement fault tolerance in the cloud environment. Many studies focus only on fault detection and do not consider fault tolerance. For this reason, in this paper, in addition to recognizing the nature of the fault, a fuzzy logic-based approach is proposed to provide an appropriate response and increase the fault tolerance in the cloud environment. Checkpoint-based migration technique is used to increase fault tolerance. Using a checkpoint during migration can reduce time and processing costs and balance the load between virtual machines in the event of a fault. The simulation is performed according to the data center of Vietnam Telecommunications Company (VDC). The results of the proposed method in a period of 60 minutes show 98.03% fault detection accuracy, which is 4.5% and 4.1% superior to FLPT and PLBFT algorithms, respectively.
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
International Journal of Intelligent Systems and Applications in Engineering
International Journal of Intelligent Systems and Applications in Engineering Computer Science-Computer Graphics and Computer-Aided Design
CiteScore
1.30
自引率
0.00%
发文量
18
期刊最新文献
Predicting Automobile Stock Prices Index in the Tehran Stock Exchange Using Machine Learning Models A Hybrid Unsupervised Density-based Approach with Mutual Information for Text Outlier Detection Digital Control and Management of Water Supply Infrastructure Using Embedded Systems and Machine Learning Machine Learning for Weather Forecasting: XGBoost vs SVM vs Random Forest in Predicting Temperature for Visakhapatnam An Enhanced Approach to Recommend Data Structures and Algorithms Problems Using Content-based Filtering
×
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