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The impact of modern cloud technologies on the efficiency of DevOps processes

НазваThe impact of modern cloud technologies on the efficiency of DevOps processes
Назва англійськоюThe impact of modern cloud technologies on the efficiency of DevOps processes
АвториMykhailo Luchkevych, Iryna Shakleina, Oleksii Duda
ПринадлежністьLviv Polytechnic National University, Lviv, Ukraine Ternopil Ivan Puluj National Technical University, Ternopil, Ukraine
Бібліографічний описThe impact of modern cloud technologies on the efficiency of DevOps processes / Mykhailo Luchkevych, Iryna Shakleina, Oleksii Duda // Scientific Journal of TNTU. — Tern.: TNTU, 2025. — Vol 117. — No 1. — P. 112–122.
Bibliographic description:Luchkevych M., Shakleina I., Duda O. (2025) The impact of modern cloud technologies on the efficiency of DevOps processes. Scientific Journal of TNTU (Tern.), vol 117, no 1, pp. 112–122.
DOI: https://doi.org/10.33108/visnyk_tntu2025.01.112
УДК

004.75:004.42

Ключові слова

DevOps, cloud technologies, AWS, GCP, Azure, CI/CD, automation, containerization, scalability, monitoring.

The article investigates the impact of modern cloud technologies on the efficiency of DevOps processes, which are key to the automation, flexibility and reliability of software development. With the rapid development of information technology, cloud platforms play an important role in accelerating software product development, testing and deployment. Implementing DevOps in the cloud environment reduces the time to market, increases the stability of software systems and optimizes the use of computing resources, which is critical for modern companies. This paper discusses DevOps teams' main challenges, including the need to scale infrastructure rapidly, ensure continuous integration and delivery (CI/CD), automate testing, monitor performance, and optimize costs. Particular attention is paid to security problems, configuration management and the human factor minimization when implementing program code changes. A comparative analysis of the three leading cloud platforms - Amazon Web Services, Google Cloud Platform, and Microsoft Azure – is carried out in the context of their impact on DevOps processes. The possibilities of automation, support for container technologies, infrastructure scalability, monitoring tools, and integration with CI/CD pipelines are evaluated. AWS was found to offer the broadest set of DevOps tools with a high level of automation, making it an attractive choice for organizations focused on complex enterprise solutions. Google Cloud Platform demonstrates the best support for Kubernetes and containerization, an essential factor for teams working with microservices architecture. Microsoft Azure provides the most profound integration with the Microsoft ecosystem and is the best choice for companies that use Windows and related products. An experimental study has shown that the choice of a cloud platform significantly impacts the speed of release cycles, the stability of the deployed infrastructure, the ability to respond quickly to changes in load, and the productivity of DevOps teams in general. Recommendations for choosing the optimal cloud platform are proposed depending on the specifics of the project, the scale of the organization, the level of system load, and automation requirements.

ISSN:2522-4433
Перелік літератури
1. El Aouni F. et al. (2024). A systematic literature review on Agile, Cloud, and DevOps integration: Challenges, benefits. Information and Software Technology, pp. 107569. Available at:https://doi.org/ 10.1016/j.infsof.2024.107569.
2. Battina D. S. Devops (2020) A New Approach To Cloud Development & Testing. nternational Journal of Emerging Technologies and Innovative Research (www. jetir. org), ISSN. pp. 2349–5162. Available at: http://www.jetir. org/papers/JETIR2008432.pdf.
3. Sravan S. S. et al. Significant Challenges to espouse DevOps Culture in Software Organisations By AWS: A methodical Review. 2023 9th International conference on advanced computing and communication systems (ICACCS). IEEE, 2023, vol. 1, pp. 395–401. Doi: 10.1109/ICACCS57279.2023.10113021.
4. Raheja Y., Borgese G., Felsen N. (2018). Effective DevOps with AWS: Implement continuous delivery and integration in the AWS environment. Packt Publishing Ltd, 363 p.
5. Alalawi A., Mohsin A., Jassim A. A survey for AWS cloud development tools and services. IET Conference Proceedings CP777. Stevenage, UK: The Institution of Engineering and Technology, 2020, vol. 2020, no. 6, pp. 17–23. Available at: https://doi.org/10.1049/icp.2021.0898.
6. Campbell B., Campbell B. (2020). CloudFormation In-Depth //The Definitive Guide to AWS Infrastructure Automation: Craft Infrastructure-as-Code Solutions, pp. 55–122. Available at: https://doi.org/10.1007/978-1-4842-5398-4_3.
7. Mishra P. (2023). Advanced AWS Services. Cloud Computing with AWS: Everything You Need to Know to be an AWS Cloud Practitioner. Berkeley, CA: Apress, pp. 247–277. Available at: https://doi.org/10. 1007/978-1-4842-9172-6_9.
8. Lekkala C. (2023) Deploying and Managing Containerized Data Workloads on Amazon EKS. J Arti Inte & Cloud Comp, vol. 2, no. 2, pp. 1–5. Available at: http://dx.doi.org/10.2139/ssrn.4908416.
9. Singh C. et al. (2019) Comparison of different CI/CD tools integrated with cloud platform. 2019 9th International Conference on Cloud Computing, Data Science & Engineering (Confluence). IEEE, pp. 7–12. Doi: 10.1109/CONFLUENCE.2019.8776985.
10. Shah J., Dubaria D. (2019) Building modern clouds: using docker, kubernetes & Google cloud platform. 2019 IEEE 9th Annual Computing and Communication Workshop and Conference (CCWC). IEEE, pp. 0184–0189. Doi: 10.1109/CCWC.2019.8666479.
11. Bisong E., Bisong E. (2019). Containers and google kubernetes engine. Building Machine Learning and Deep Learning Models on Google Cloud Platform: A Comprehensive Guide for Beginners, pp. 655–670. Available at: https://doi.org/10.1007/978-1-4842-4470-8_45.
12. Esfahani H. et al. (2016). CloudBuild: Microsoft's distributed and caching build service. Proceedings of the 38th International Conference on Software Engineering Companion, pp. 11–20. Available at: https://doi. org/10.1145/2889160.2889222.
13. Sukhdeve D. S. R., Sukhdeve S. S. (2023). Introduction to GCP. Google Cloud Platform for Data Science: A Crash Course on Big Data, Machine Learning, and Data Analytics Services. Berkeley, CA: Apress, pp. 1–9. Available at: https://doi.org/10.1007/978-1-4842-9688-2_1.
14. Riti P., Riti P. (2018). Monitoring in GCP //Pro DevOps with Google Cloud Platform: With Docker, Jenkins, and Kubernetes, pp. 165–190. Available at: https://doi.org/10.1007/978-1-4842-3897-4_7.
15. Wang I. (2024). Provisioning Infrastructure on GCP //Terraform Made Easy: Provisioning, Managing and Automating Cloud Infrastructure with Terraform on Google Cloud. Berkeley, CA: Apress, pp. 95–170. Available at: https://doi.org/10.1007/979-8-8688-1010-7_4.
16. Barrientos A., Duran Huanca J. G., Mayta Segovia H. A. (2023). Implementation of a Software Engineering Model with DevOps on Microsoft Azure. Proceedings of the 2023 8th International Conference on Information Systems Engineering, pp. 1–6. Available at: https://doi.org/10.1145/36 41032.3641037.
17. Narayanan P. K. (2024). Engineering Data Pipelines Using Microsoft Azure. Data Engineering for Machine Learning Pipelines: From Python Libraries to ML Pipelines and Cloud Platforms. Berkeley, CA: Apress, pp. 571–616. Available at: https://doi.org/10.1007/979-8-8688-0602-5_17.
18. Satapathi A., Mishra A. (2022). Deploy an ASP. NET Web Application to an Azure Web App Using GitHub Actions. Developing Cloud-Native Solutions with Microsoft Azure and. NET: Build Highly Scalable Solutions for the Enterprise. Berkeley, CA: Apress, pp. 249–270. Available at: https://doi.org/ 10.1007/978-1-4842-9004-0_11.
19. Chandrasekara C. et al. (2020). Getting Started with Azure Git Repos. Hands-on Azure Repos: Understanding Centralized and Distributed Version Control in Azure DevOps Services, pp. 139–170. Available at: https://doi.org/10.1007/978-1-4842-5425-7_7.
20. Sahay R., Sahay R. (2020). Azure monitoring. Microsoft Azure Architect Technologies Study Companion:Hands
-on Preparation and Practice for Exam AZ-300 and AZ-303, pp. 139–167. Available at: https://doi. org/10.1007/978-1-4842-6200-9_5.
21. Satapathi A., Mishra A. (2021). Enabling Application Insights and Azure Monitor. Hands-on Azure Functions with C#. Apress, Berkeley, CA, pp. 233–261. Available at: https://doi.org/10.1007/978-1-4842-7122-3_10.
22. Chawla H. et al. (2019). Azure kubernetes service. Building Microservices Applications on Microsoft Azure: Designing, Developing, Deploying, and Monitoring, pp. 151–177. Available at: https://doi.org/ 10.1007/978-1-4842-4828-7_5.
23. Borra P. (2024) Comparison and Analysis of Leading Cloud Service Providers (AWS, Azure and GCP). International Journal of Advanced Research in Engineering and Technology (IJARET), vol. 15, no. 3, pp. 266–278. Available at: https://doi.org/10.17605/OSF.IO/T2DHW.
24. Borra P. (2024) Comparative Review: Top Cloud Service Providers ETL Tools-AWS vs. Azure vs. GCP. International Journal of Computer Engineering and Technology (IJCET), vol. 15, pp. 203–208. Available at: https://doi.org/10.17605/OSF.IO/X7WCT.
25. Kingsley M. S. (2023). Comparing AWS, Azure, and GCP. Cloud Technologies and Services: Theoretical Concepts and Practical Applications. Cham: Springer International Publishing, pp. 381–393. Available at: https://doi.org/10.1007/978-3-031-33669-0_12.
References:
1. El Aouni F. et al. (2024). A systematic literature review on Agile, Cloud, and DevOps integration: Challenges, benefits. Information and Software Technology, pp. 107569. Available at:https://doi.org/ 10.1016/j.infsof.2024.107569.
2. Battina D. S. Devops (2020) A New Approach To Cloud Development & Testing. nternational Journal of Emerging Technologies and Innovative Research (www. jetir. org), ISSN. pp. 2349–5162. Available at: http://www.jetir. org/papers/JETIR2008432.pdf.
3. Sravan S. S. et al. Significant Challenges to espouse DevOps Culture in Software Organisations By AWS: A methodical Review. 2023 9th International conference on advanced computing and communication systems (ICACCS). IEEE, 2023, vol. 1, pp. 395–401. Doi: 10.1109/ICACCS57279.2023.10113021.
4. Raheja Y., Borgese G., Felsen N. (2018). Effective DevOps with AWS: Implement continuous delivery and integration in the AWS environment. Packt Publishing Ltd, 363 p.
5. Alalawi A., Mohsin A., Jassim A. A survey for AWS cloud development tools and services. IET Conference Proceedings CP777. Stevenage, UK: The Institution of Engineering and Technology, 2020, vol. 2020, no. 6, pp. 17–23. Available at: https://doi.org/10.1049/icp.2021.0898.
6. Campbell B., Campbell B. (2020). CloudFormation In-Depth //The Definitive Guide to AWS Infrastructure Automation: Craft Infrastructure-as-Code Solutions, pp. 55–122. Available at: https://doi.org/10.1007/978-1-4842-5398-4_3.
7. Mishra P. (2023). Advanced AWS Services. Cloud Computing with AWS: Everything You Need to Know to be an AWS Cloud Practitioner. Berkeley, CA: Apress, pp. 247–277. Available at: https://doi.org/10. 1007/978-1-4842-9172-6_9.
8. Lekkala C. (2023) Deploying and Managing Containerized Data Workloads on Amazon EKS. J Arti Inte & Cloud Comp, vol. 2, no. 2, pp. 1–5. Available at: http://dx.doi.org/10.2139/ssrn.4908416.
9. Singh C. et al. (2019) Comparison of different CI/CD tools integrated with cloud platform. 2019 9th International Conference on Cloud Computing, Data Science & Engineering (Confluence). IEEE, pp. 7–12. Doi: 10.1109/CONFLUENCE.2019.8776985.
10. Shah J., Dubaria D. (2019) Building modern clouds: using docker, kubernetes & Google cloud platform. 2019 IEEE 9th Annual Computing and Communication Workshop and Conference (CCWC). IEEE, pp. 0184–0189. Doi: 10.1109/CCWC.2019.8666479.
11. Bisong E., Bisong E. (2019). Containers and google kubernetes engine. Building Machine Learning and Deep Learning Models on Google Cloud Platform: A Comprehensive Guide for Beginners, pp. 655–670. Available at: https://doi.org/10.1007/978-1-4842-4470-8_45.
12. Esfahani H. et al. (2016). CloudBuild: Microsoft's distributed and caching build service. Proceedings of the 38th International Conference on Software Engineering Companion, pp. 11–20. Available at: https://doi. org/10.1145/2889160.2889222.
13. Sukhdeve D. S. R., Sukhdeve S. S. (2023). Introduction to GCP. Google Cloud Platform for Data Science: A Crash Course on Big Data, Machine Learning, and Data Analytics Services. Berkeley, CA: Apress, pp. 1–9. Available at: https://doi.org/10.1007/978-1-4842-9688-2_1.
14. Riti P., Riti P. (2018). Monitoring in GCP //Pro DevOps with Google Cloud Platform: With Docker, Jenkins, and Kubernetes, pp. 165–190. Available at: https://doi.org/10.1007/978-1-4842-3897-4_7.
15. Wang I. (2024). Provisioning Infrastructure on GCP //Terraform Made Easy: Provisioning, Managing and Automating Cloud Infrastructure with Terraform on Google Cloud. Berkeley, CA: Apress, pp. 95–170. Available at: https://doi.org/10.1007/979-8-8688-1010-7_4.
16. Barrientos A., Duran Huanca J. G., Mayta Segovia H. A. (2023). Implementation of a Software Engineering Model with DevOps on Microsoft Azure. Proceedings of the 2023 8th International Conference on Information Systems Engineering, pp. 1–6. Available at: https://doi.org/10.1145/36 41032.3641037.
17. Narayanan P. K. (2024). Engineering Data Pipelines Using Microsoft Azure. Data Engineering for Machine Learning Pipelines: From Python Libraries to ML Pipelines and Cloud Platforms. Berkeley, CA: Apress, pp. 571–616. Available at: https://doi.org/10.1007/979-8-8688-0602-5_17.
18. Satapathi A., Mishra A. (2022). Deploy an ASP. NET Web Application to an Azure Web App Using GitHub Actions. Developing Cloud-Native Solutions with Microsoft Azure and. NET: Build Highly Scalable Solutions for the Enterprise. Berkeley, CA: Apress, pp. 249–270. Available at: https://doi.org/ 10.1007/978-1-4842-9004-0_11.
19. Chandrasekara C. et al. (2020). Getting Started with Azure Git Repos. Hands-on Azure Repos: Understanding Centralized and Distributed Version Control in Azure DevOps Services, pp. 139–170. Available at: https://doi.org/10.1007/978-1-4842-5425-7_7.
20. Sahay R., Sahay R. (2020). Azure monitoring. Microsoft Azure Architect Technologies Study Companion:Hands
-on Preparation and Practice for Exam AZ-300 and AZ-303, pp. 139–167. Available at: https://doi. org/10.1007/978-1-4842-6200-9_5.
21. Satapathi A., Mishra A. (2021). Enabling Application Insights and Azure Monitor. Hands-on Azure Functions with C#. Apress, Berkeley, CA, pp. 233–261. Available at: https://doi.org/10.1007/978-1-4842-7122-3_10.
22. Chawla H. et al. (2019). Azure kubernetes service. Building Microservices Applications on Microsoft Azure: Designing, Developing, Deploying, and Monitoring, pp. 151–177. Available at: https://doi.org/ 10.1007/978-1-4842-4828-7_5.
23. Borra P. (2024) Comparison and Analysis of Leading Cloud Service Providers (AWS, Azure and GCP). International Journal of Advanced Research in Engineering and Technology (IJARET), vol. 15, no. 3, pp. 266–278. Available at: https://doi.org/10.17605/OSF.IO/T2DHW.
24. Borra P. (2024) Comparative Review: Top Cloud Service Providers ETL Tools-AWS vs. Azure vs. GCP. International Journal of Computer Engineering and Technology (IJCET), vol. 15, pp. 203–208. Available at: https://doi.org/10.17605/OSF.IO/X7WCT.
25. Kingsley M. S. (2023). Comparing AWS, Azure, and GCP. Cloud Technologies and Services: Theoretical Concepts and Practical Applications. Cham: Springer International Publishing, pp. 381–393. Available at: https://doi.org/10.1007/978-3-031-33669-0_12.
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