基于云的软件开发生命周期:一种基于度量分析的云服务提供商评估简化算法

IF 7.7 1区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Big Data Mining and Analytics Pub Date : 2023-01-26 DOI:10.26599/BDMA.2022.9020016
Santhosh S;Narayana Swamy Ramaiah
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引用次数: 0

摘要

目前,全球市场上有数百家云供应商根据客户的需求提供各种服务。在服务数量、基础设施可用性、安全策略、每位客户的成本和市场声誉方面,并非所有云供应商都是一样的。因此,软件开发人员和组织在为其开发活动选择合适的云供应商时面临两难境地。因此,在选择合适的供应商之前,需要评估各种云服务提供商(CSP)和平台。现有的解决方案要么基于符合要求的模拟工具,要么根据服务质量属性进行评估。然而,他们需要更多的时间来收集数据、模拟和评估供应商。基于比较、参数排名和分配的权重,拟议的工作在主要指标方面对各种CSP进行了比较,如建立、服务、基础设施、工具、定价模型、市场份额等。此外,根据优先级对参数进行分类。计算每个CSP的加权平均值,然后按降序对值进行排序。实验结果表明,基于所选参数对CSP进行了无偏选择。所提出的参数排序优先级加权(PRPLW)算法根据软件开发的要求简化了最适合云供应商的选择。
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Cloud-Based Software Development Lifecycle: A Simplified Algorithm for Cloud Service Provider Evaluation with Metric Analysis
At present, hundreds of cloud vendors in the global market provide various services based on a customer's requirements. All cloud vendors are not the same in terms of the number of services, infrastructure availability, security strategies, cost per customer, and reputation in the market. Thus, software developers and organizations face a dilemma when choosing a suitable cloud vendor for their developmental activities. Thus, there is a need to evaluate various cloud service providers (CSPs) and platforms before choosing a suitable vendor. Already existing solutions are either based on simulation tools as per the requirements or evaluated concerning the quality of service attributes. However, they require more time to collect data, simulate and evaluate the vendor. The proposed work compares various CSPs in terms of major metrics, such as establishment, services, infrastructure, tools, pricing models, market share, etc., based on the comparison, parameter ranking, and weightage allocated. Furthermore, the parameters are categorized depending on the priority level. The weighted average is calculated for each CSP, after which the values are sorted in descending order. The experimental results show the unbiased selection of CSPs based on the chosen parameters. The proposed parameter-ranking priority level weightage (PRPLW) algorithm simplifies the selection of the best-suited cloud vendor in accordance with the requirements of software development.
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来源期刊
Big Data Mining and Analytics
Big Data Mining and Analytics Computer Science-Computer Science Applications
CiteScore
20.90
自引率
2.20%
发文量
84
期刊介绍: Big Data Mining and Analytics, a publication by Tsinghua University Press, presents groundbreaking research in the field of big data research and its applications. This comprehensive book delves into the exploration and analysis of vast amounts of data from diverse sources to uncover hidden patterns, correlations, insights, and knowledge. Featuring the latest developments, research issues, and solutions, this book offers valuable insights into the world of big data. It provides a deep understanding of data mining techniques, data analytics, and their practical applications. Big Data Mining and Analytics has gained significant recognition and is indexed and abstracted in esteemed platforms such as ESCI, EI, Scopus, DBLP Computer Science, Google Scholar, INSPEC, CSCD, DOAJ, CNKI, and more. With its wealth of information and its ability to transform the way we perceive and utilize data, this book is a must-read for researchers, professionals, and anyone interested in the field of big data analytics.
期刊最新文献
Contents Front Cover Incremental Data Stream Classification with Adaptive Multi-Task Multi-View Learning Attention-Based CNN Fusion Model for Emotion Recognition During Walking Using Discrete Wavelet Transform on EEG and Inertial Signals Gender-Based Analysis of User Reactions to Facebook Posts
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