{"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}
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.
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
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-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