基于随机微分方程和努力数据的边缘计算性能评估

IF 1.5 4区 计算机科学 Q3 COMPUTER SCIENCE, SOFTWARE ENGINEERING Software Testing Verification & Reliability Pub Date : 2021-02-15 DOI:10.1002/stvr.1766
Y. Tamura, S. Yamada
{"title":"基于随机微分方程和努力数据的边缘计算性能评估","authors":"Y. Tamura, S. Yamada","doi":"10.1002/stvr.1766","DOIUrl":null,"url":null,"abstract":"Many open‐source software are included in commercial software. Also, several open‐source software are used in the cloud service such as OpenStack and Eucalyptus from standpoint of the unified management, cost reduction and maintainability. In particular, the operation phase of cloud service has a unique feature with uncertainty such as big data and network connectivity, because the operation phase of cloud service changes depending on many external factors. On the other hand, the effective methods of performance assessments for cloud service have only a few presented. Recently, edge computing is the focus of attention because of the problems of connection and processing delay in case of cloud computing. It is known as that cloud computing treats big data. On the other hand, edge computing operates on instant data. We focus on the performance assessments based on the relationship between the cloud and edge services operated by using several open‐source software. Then we propose a two‐dimensional stochastic differential equation model considering the unique features with uncertainty from big data under the operation of cloud and edge services. Also, we analyse actual data to show numerical examples of performance assessments considering the network connectivity as characteristics of cloud and edge services. Moreover, we compare the noise terms of the proposed model for actual data.","PeriodicalId":49506,"journal":{"name":"Software Testing Verification & Reliability","volume":"150 1","pages":""},"PeriodicalIF":1.5000,"publicationDate":"2021-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":"{\"title\":\"Performance assessment based on stochastic differential equation and effort data for edge computing\",\"authors\":\"Y. Tamura, S. Yamada\",\"doi\":\"10.1002/stvr.1766\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Many open‐source software are included in commercial software. Also, several open‐source software are used in the cloud service such as OpenStack and Eucalyptus from standpoint of the unified management, cost reduction and maintainability. In particular, the operation phase of cloud service has a unique feature with uncertainty such as big data and network connectivity, because the operation phase of cloud service changes depending on many external factors. On the other hand, the effective methods of performance assessments for cloud service have only a few presented. Recently, edge computing is the focus of attention because of the problems of connection and processing delay in case of cloud computing. It is known as that cloud computing treats big data. On the other hand, edge computing operates on instant data. We focus on the performance assessments based on the relationship between the cloud and edge services operated by using several open‐source software. Then we propose a two‐dimensional stochastic differential equation model considering the unique features with uncertainty from big data under the operation of cloud and edge services. Also, we analyse actual data to show numerical examples of performance assessments considering the network connectivity as characteristics of cloud and edge services. Moreover, we compare the noise terms of the proposed model for actual data.\",\"PeriodicalId\":49506,\"journal\":{\"name\":\"Software Testing Verification & Reliability\",\"volume\":\"150 1\",\"pages\":\"\"},\"PeriodicalIF\":1.5000,\"publicationDate\":\"2021-02-15\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"3\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Software Testing Verification & Reliability\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://doi.org/10.1002/stvr.1766\",\"RegionNum\":4,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"COMPUTER SCIENCE, SOFTWARE ENGINEERING\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Software Testing Verification & Reliability","FirstCategoryId":"94","ListUrlMain":"https://doi.org/10.1002/stvr.1766","RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"COMPUTER SCIENCE, SOFTWARE ENGINEERING","Score":null,"Total":0}
引用次数: 3

摘要

许多开源软件都包含在商业软件中。此外,从统一管理、降低成本和可维护性的角度来看,云服务中使用了一些开源软件,如OpenStack和Eucalyptus。特别是云服务的运营阶段具有大数据、网络连通性等不确定性的独特特征,因为云服务的运营阶段的变化取决于许多外部因素。另一方面,有效的云服务绩效评估方法却很少。最近,由于云计算的连接和处理延迟问题,边缘计算成为人们关注的焦点。众所周知,云计算处理大数据。另一方面,边缘计算对即时数据进行操作。我们专注于基于使用几个开源软件运行的云和边缘服务之间关系的性能评估。在此基础上,提出了考虑云计算和边缘服务下大数据不确定性的二维随机微分方程模型。此外,我们还分析了实际数据,以显示考虑网络连接作为云和边缘服务特征的性能评估的数值示例。此外,我们将所提出的模型的噪声项与实际数据进行比较。
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
Performance assessment based on stochastic differential equation and effort data for edge computing
Many open‐source software are included in commercial software. Also, several open‐source software are used in the cloud service such as OpenStack and Eucalyptus from standpoint of the unified management, cost reduction and maintainability. In particular, the operation phase of cloud service has a unique feature with uncertainty such as big data and network connectivity, because the operation phase of cloud service changes depending on many external factors. On the other hand, the effective methods of performance assessments for cloud service have only a few presented. Recently, edge computing is the focus of attention because of the problems of connection and processing delay in case of cloud computing. It is known as that cloud computing treats big data. On the other hand, edge computing operates on instant data. We focus on the performance assessments based on the relationship between the cloud and edge services operated by using several open‐source software. Then we propose a two‐dimensional stochastic differential equation model considering the unique features with uncertainty from big data under the operation of cloud and edge services. Also, we analyse actual data to show numerical examples of performance assessments considering the network connectivity as characteristics of cloud and edge services. Moreover, we compare the noise terms of the proposed model for actual data.
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
Software Testing Verification & Reliability
Software Testing Verification & Reliability 工程技术-计算机:软件工程
CiteScore
3.70
自引率
0.00%
发文量
34
审稿时长
>12 weeks
期刊介绍: The journal is the premier outlet for research results on the subjects of testing, verification and reliability. Readers will find useful research on issues pertaining to building better software and evaluating it. The journal is unique in its emphasis on theoretical foundations and applications to real-world software development. The balance of theory, empirical work, and practical applications provide readers with better techniques for testing, verifying and improving the reliability of software. The journal targets researchers, practitioners, educators and students that have a vested interest in results generated by high-quality testing, verification and reliability modeling and evaluation of software. Topics of special interest include, but are not limited to: -New criteria for software testing and verification -Application of existing software testing and verification techniques to new types of software, including web applications, web services, embedded software, aspect-oriented software, and software architectures -Model based testing -Formal verification techniques such as model-checking -Comparison of testing and verification techniques -Measurement of and metrics for testing, verification and reliability -Industrial experience with cutting edge techniques -Descriptions and evaluations of commercial and open-source software testing tools -Reliability modeling, measurement and application -Testing and verification of software security -Automated test data generation -Process issues and methods -Non-functional testing
期刊最新文献
Model‐based testing, test case prioritization and testing of virtual reality applications In vivo testing and integration of proving and testing Mutation testing optimisations using the Clang front‐end Semantic‐aware two‐phase test case prioritization for continuous integration Exploiting deep reinforcement learning and metamorphic testing to automatically test virtual reality 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