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Open Access

Cloud-Based Software Development Lifecycle: A Simplified Algorithm for Cloud Service Provider Evaluation with Metric Analysis

Computer Science & Engineering, Jain (Deemed-to-be University), Bangalore 562112, India
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Abstract

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.

References

[1]
S. Santhosh and N. Ramaiah, The impact of software engineering methods for cloud computing models – A survey, https://dx.doi.org/10.2139/ssrn.3372019, 2019.
[2]
S. Santhosh and N. S. Ramaiah, Understanding the significant challenges of software engineering in cloud environments, in Computational Intelligence Techniques and Their Applications to Software Engineering Problems, A. Bansal, A. Jain, S. Jain, V. Jain, and A. Choudhary, Eds. Boca Raton, FL, USA: CRC Press, 2020.
[4]
N. Upadhyay, Managing cloud service evaluation and selection, Procedia Comput. Sci., vol. 122, pp. 10611068, 2017.
[5]
Y. Wang, J. Wen, X. Wang, B. Tao, and W. Zhou, A cloud service trust evaluation model based on combining weights and gray correlation analysis, Secur. Commun. Networks, vol. 2019, p. 2437062, 2019.
[6]
S. Wibowo, H. Deng, and W. Xu, Evaluation of cloud services: A fuzzy multi-criteria group decision making method, Algorithms, vol. 9, no. 4, p. 84, 2016.
[7]
M. Eisa, M. Younas, K. Basu, and I. Awan, Modelling and simulation of QoS-aware service selection in cloud computing. Simul. Modell. Pract. Theory, vol. 103, p. 102108, 2020.
[8]
R. El-Gazzar, E. Hustad, and D. H. Olsen, An institutional lens on cloud computing adoption – A study of institutional factors and adoption strategies, in Proc. 25th European Conf. Information Systems (ECIS), Guimarães, Portugal, 2017, pp. 24772492.
[9]
M. Abourezq and A. Idrissi, Integration of QoS aspects in the cloud service research and selection system, Int. J. Adv. Comput. Sci. Appl., vol. 6, no. 6, pp. 111122, 2015.
[10]
D. Schlagwein and A. Thorogood, Married for life? A cloud computing client–provider relationship continuance model, in Proc. 22nd European Conf. Information Systems, Tel Aviv, Israel, 2014.
[11]
Intellipaat, AWS vs Azure vs Google Cloud: Choosing the Right Cloud Platform, https://intellipaat.com/blog/aws-vs-azure-vs-google-cloud/, 2022.
[12]
AWS Cloud Products, https://aws.amazon.com/products/, 2022.
[13]
Microsoft Azure, https://azure.microsoft.com/, 2022.
[14]
Google Cloud, https://cloud.google.com/products/, 2022.
[15]
A. Bartwal, AWS vs Azure vs GCP & AWS vs Azure vs GCP Comparison Overview, https://k21academy.com/amazon-web-services/aws-solutions-architect/aws-vs-azure-vs-gcp/, 2021.
[16]
C. Harvey, AWS vs. Azure vs. Google Cloud: 2022 cloud platform comparison, https://www.datamation.com/cloud/aws-vs-azure-vs-google-cloud/, 2021.
Big Data Mining and Analytics
Pages 127-138
Cite this article:
S S, Ramaiah NS. Cloud-Based Software Development Lifecycle: A Simplified Algorithm for Cloud Service Provider Evaluation with Metric Analysis. Big Data Mining and Analytics, 2023, 6(2): 127-138. https://doi.org/10.26599/BDMA.2022.9020016

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Received: 05 April 2022
Revised: 30 May 2022
Accepted: 20 June 2022
Published: 26 January 2023
© The author(s) 2023.

The articles published in this open access journal are distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/).

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