An Overview of Different Automation Strategies Used in Cloud-Based CI/CD Pipelines for Software Deployment
DOI:
https://doi.org/10.21590/ijtmh.2023090108Keywords:
Artificial Intelligence, Internet of Things, Cloud Computing, DevOps, CI/CD Pipelines, Automation, Predictive AnalyticsAbstract
Modern application development and deployment has become exceedingly complex due to the rapid rise of cloud computing, the Internet of Things (IoT), and distributed software systems. The traditional software delivery architecture and centralized design can barely meet the requirement to handle huge amounts of data, the frequent code updates, and the dynamic architecture requirements. Automation, collaboration, and rapid software development have emerged as powerful tools for overcoming these challenges, thanks to the ideas of DevOps and CI/CD pipelines. This research paper delves deeply into cloud-based continuous integration and continuous delivery (CI/CD) pipelines, specifically focusing on automation solutions that improve software deployment efficiency, reliability, and scalability. It addresses the fundamentals of CI/CD, the key aspects of automation, such as build automation, automated testing, infrastructure automation, deployment automation, and security automation (DevSecOps) and the most popular tools that support such operations. Dependency management, interoperability of tools, security, and complexity of the multi-cloud are already existing problems that are discussed in the paper, as well. It also sheds some light on the new trends, such as AI/ML-powered CI/CD optimization and GitOps-powered deployments, which smarter and self-healing software delivery pipelines that are more secure. Researchers and practitioners able to use the review's organized picture of present practices and future research directions to better understand how automation is advancing in modern software systems' cloud-based CI/CD pipelines.
References
1. L. Chen, ―Microservices: Architecting for Continuous Delivery and DevOps,‖ in 2018 IEEE
International Conference on Software Architecture (ICSA), IEEE, Apr. 2018, pp. 39–397. doi:
10.1109/ICSA.2018.00013.
2. M. Shahin, M. Ali Babar, and L. Zhu, ―Continuous Integration, Delivery and Deployment: A
Systematic Review on Approaches, Tools, Challenges and Practices,‖ IEEE Access, vol. 5, pp.
3909–3943, 2017, doi: 10.1109/ACCESS.2017.2685629.
3. P. Srivastava and R. Khan, ―A Review Paper on Cloud Computing,‖ Int. J. Adv. Res. Comput.
Sci. Softw. Eng., 2018, doi: 10.23956/ijarcsse.v8i6.711.
4. E. Laukkanen, J. Itkonen, and C. Lassenius, ―Problems, causes and solutions when adopting
continuous delivery—A systematic literature review,‖ Inf. Softw. Technol., vol. 82, pp. 55–79,
Feb. 2017, doi: 10.1016/j.infsof.2016.10.001.
5. Jula, E. Sundararajan, and Z. Othman, ―Cloud computing service composition: A systematic literature
review,‖ Expert Syst. Appl., vol. 41, no. 8, pp. 3809–3824, Jun. 2014, doi: 10.1016/j.eswa.2013.12.017.
6. L. Chen, ―Continuous Delivery: Overcoming adoption challenges,‖ J. Syst. Softw., vol. 128, pp.
72–86, Jun. 2017, doi: 10.1016/j.jss.2017.02.013.
7. S. A. I. B. S. Arachchi and I. Perera, ―Continuous Integration and Continuous Delivery Pipeline
Automation for Agile Software Project Management,‖ in 2018 Moratuwa Engineering Research
Conference (MERCon), IEEE, May 2018, pp. 156–161. doi: 10.1109/MERCon.2018.8421965. 8. W. Venters and E. A. Whitley, ―A critical review of cloud computing: Researching desires and
realities,‖ J. Inf. Technol., vol. 27, no. 3, pp. 179–197, 2012, doi: 10.1057/jit.2012.17.
9. L. Chen, ―Continuous Delivery: Huge Benefits, but Challenges Too,‖ IEEE Softw., vol. 32, no. 2,
pp. 50–54, Mar. 2015, doi: 10.1109/MS.2015.27.
10. Y. SKA and J. P, ―A Study And Analysis Of Continuous Delivery, Continuous Integration In
Software Development Environment,‖ J. Emerg. Technol. Innov. Res., vol. 6, no. 9, 2019.
11. S. Garg, ―Predictive Analytics and Auto Remediation using Artificial Inteligence and Machine
learning in Cloud Computing Operations,‖ Int. J. Innov. Res. Eng. Multidiscip. Phys. Sci., vol. 7,
no. 2, 2019, doi: 10.5281/zenodo.15362327.
12. M. L. G. Nerella, ―Automated Cross-Platform Database Migration And High Availability
Implementation,‖ Turkish J. Comput. Math. Educ., vol. 9, no. 2, pp. 823–835, Jul. 2018, doi:
10.61841/turcomat.v9i2.15284.
13. Kushwaha, P. Pathak, and S. Gupta, ―Review of Optimize Load Balancing Algorithms in Cloud.,‖
Int. J. Distrib. Cloud Comput., vol. 4, no. 2, p. 1, 2016.
14. R. Nirek, ―Challenges and Solutions for Implementing CI/CD Pipelines in Linux-Based
Development Frameworks,‖ J. Sci. Eng. Res., vol. 6, no. 6, pp. 229–232, 2019, doi:
10.13140/RG.2.2.20819.80161.
15. T. Tegeler, F. Gossen, and B. Steffen, ―A Model-driven Approach to Continuous Practices for
Modern Cloud-based Web Applications,‖ in 2019 9th International Conference on Cloud
Computing, Data Science & Engineering (Confluence), IEEE, Jan. 2019, pp. 1–6. doi:
10.1109/CONFLUENCE.2019.8776962.
16. K. Gallaba, ―Improving the Robustness and Efficiency of Continuous Integration and
Deployment,‖ in 2019 IEEE International Conference on Software Maintenance and Evolution (ICSME), IEEE, Sep. 2019, pp. 619–623. doi: 10.1109/ICSME.2019.00099.
17. L. G. Guseila, D.-V. Bratu, and S.-A. Moraru, ―DevOps Transformation for Multi-Cloud IoT
Applications,‖ in 2019 International Conference on Sensing and Instrumentation in IoT Era
(ISSI), IEEE, Aug. 2019, pp. 1–6. doi: 10.1109/ISSI47111.2019.9043730.
18. Singh, N. S. Gaba, M. Kaur, and B. Kaur, ―Comparison of Different CI/CD Tools Integrated with Cloud
Platform,‖ in 2019 9th International Conference on Cloud Computing, Data Science & Engineering
(Confluence), IEEE, Jan. 2019, pp. 7–12. doi:
10.1109/CONFLUENCE.2019.8776985.
19. T. F. Düllmann, C. Paule, and A. van Hoorn, ―Exploiting DevOps Practices for Dependable and
Secure Continuous Delivery Pipelines,‖ in 2018 IEEE/ACM 4th International Workshop on
Rapid Continuous Software Engineering (RCoSE), 2018, pp. 27–30.
20. H. Yasar, ―Experiment: Sizing Exposed Credentials in GitHub Public Repositories for CI/CD,‖
in 2018 IEEE Cybersecurity Development (SecDev), IEEE, Sep. 2018, pp. 143– 143. doi:
10.1109/SecDev.2018.00039.
21. F. Nogueira, J. C.B. Ribeiro, M. A. Zenha-Rela, and A. Craske, ―Improving La Redoute’s CI/CD
Pipeline and DevOps Processes by Applying Machine Learning Techniques,‖ in 2018 11th
International Conference on the Quality of Information and Communications Technology
(QUATIC), IEEE, Sep. 2018, pp. 282–286. doi: 10.1109/QUATIC.2018.00050.
22. Routhu, K. K. (2022). From Case Management to Conversational HR: Redefining Help Desks
with Oracle’s AI and NLP Framework. International Journal of Science, Engineering and
Technology, 10(6).
23. Vattikonda, N., Gupta, A. K., Polu, A. R., Narra, B., Buddula, D. V. K. R., & Patchipulusu, H. H.
S. (2022). Blockchain Technology in Supply Chain and Logistics: A Comprehensive Review of
Applications, Challenges, and Innovations. International Journal of Emerging Trends in
Computer Science and Information Technology, 3(3), 72-80.
24. Attipalli, A., BITKURI, V., Mamidala, J. V., Kendyala, R., & KURMA, J. (2022). Empowering
Cloud Security with Artificial Intelligence: Detecting Threats Using Advanced Machine learning
Technologies. Available at SSRN 5741263.
25. Padur, S. K. R. (2022). Intelligent resource management: AI methods for predictive workload
forecasting in cloud data centers. J. Artif. Intell. Mach. Learn. & Data Sci, 1(1), 2936-2941.
26. Routhu, K. K. (2022). From RFID to Geofencing: IoT-Enabled Smart Time Tracking in Oracle
HCM Cloud. International Journal of Science, Engineering and Technology, 10(4).
27. Polam, R. M., Kamarthapu, B., Kakani, A. B., Nandiraju, S. K. K., Chundru, S. K., & Vangala, S.
R. (2022). Data Security in Cloud Computing: Encryption, Zero Trust, and Homomorphic
Encryption. International Journal of Emerging Trends in Computer Science and Information
Technology, 3(4), 31-41.
28. Padur, S. K. R. (2022). AI augmented platform engineering, transforming developer experience
through intelligent automation and self optimizing internal platforms. International Journal of
Science, Engineering and Technology, 10(5), 10-5281.
29. Polu, A. R., Buddula, D. V. K. R., Narra, B., Gupta, A., Vattikonda, N., & Patchipulusu, H. (2021).
Evolution of AI in Software Development and Cybersecurity: Unifying Automation, Innovation,
and Protection in the Digital Age. Available at SSRN 5266517. 30. Padur, S. K. R. (2020). From centralized control to democratized insights: Migrating enterprise
reporting from IBM Cognos to Microsoft Power BI. Int. J. Sci. Res. Comput. Sci. Eng. Inf.
Technol, 6(1), 218-225.
31. Bitkuri, V., Kendyala, R., Kurma, J., Mamidala, V., Enokkaren, S. J., & Attipalli, A. (2021).
Systematic Review of Artificial Intelligence Techniques for Enhancing Financial Reporting and
Regulatory Compliance. International Journal of Emerging Trends in Computer Science and
Information Technology, 2(4), 73-80.
32. Nalluri, S. K., Parasaram, V. K. B., & Bathini, V. T. (2021). Autonomous Manufacturing
Operations Using Intelligent MES and Cloud-Native Analytics. Journal of Multidisciplinary
Knowledge, 1(1), 45–55. Retrieved from https://jmk.datatablets.com/index.php/j/article/view/127
33. Padur, S. K. R. (2019). Machine learning for predictive capacity planning: Evolution from
analytical modeling to autonomous infrastructure. International Journal of Scientific Research in
Computer Science, Engineering and Information Technology, 5(5), 285-293.
34. Attipalli, A., Enokkaren, S., BITKURI, V., Kendyala, R., KURMA, J., & Mamidala, J. V. (2021).
Enhancing Cloud Infrastructure Security Through AI-Powered Big Data Anomaly
Detection. Available at SSRN 5741305.
35. Singh, A. A. S., Tamilmani, V., Maniar, V., Kothamaram, R. R., Rajendran, D., & Namburi, V. D.
(2021). Predictive Modeling for Classification of SMS Spam Using NLP and ML
Techniques. International Journal of Artificial Intelligence, Data Science, and Machine
Learning, 2(4), 60-69.
36. Padur, S. K. R. (2020). AI augmented disaster recovery simulations: From chaos engineering to
autonomous resilience orchestration. International Journal of Scientific Research in Science,
Engineering and Technology, 7(6), 367-378.
37. Reddy Padur, S. K. (2021). From Scripts to Platforms-as-Code: The Role of Terraform and Ansible
in Declarative Infrastructure Rollouts. International Journal of Scientific Research in Computer
Science, Engineering and Information Technology, 621-628.
38. Kothamaram, R. R., Rajendran, D., Namburi, V. D., Singh, A. A. S., Tamilmani, V., & Maniar, V.
(2021). A Survey of Adoption Challenges and Barriers in Implementing Digital Payroll
Management Systems in Across Organizations. International Journal of Emerging Research in
Engineering and Technology, 2(2), 64-72.
39. Padur, S. K. R. (2018). Autonomous cloud economics: AI driven right sizing and cost optimization
in hybrid infrastructures. International Journal of Scientific Research in Science and
Technology, 4(5), 2090-2097.
40. Rajendran, D., Namburi, V. D., Singh, A. A. S., Tamilmani, V., Maniar, V., & Kothamaram, R.
R. (2021). Anomaly Identification in IoT-Networks Using Artificial Intelligence-Based Data-
Driven Techniques in Cloud Environmen. International Journal of Emerging Trends in Computer
Science and Information Technology, 2(2), 83-91.
41. Padur, S. K. R. (2021). Bridging Human, System, and Cloud Integration through RESTful
Automation and Governance. the International Journal of Science, Engineering and
Technology, 9(6).
42. Attipalli, A., BITKURI, V., KURMA, J., Enokkaren, S., Kendyala, R., & Mamidala, J. V. (2021).
A Survey of Artificial Intelligence Methods in Liquidity Risk Management: Challenges and Future
Directions. Available at SSRN 5741342. 43. Padur, S. K. R. (2021). From Control to Code: Governance Models for Multi-Cloud ERP
Modernization. International Journal of Scientific Research & Engineering Trends, 7(3).
44. Routhu, K. K. (2021). Harnessing AI Dashboards in Oracle Cloud HCM: Advancing Predictive
Workforce Intelligence and Managerial Agility. International Journal of Scientific Research &
Engineering Trends, 7(6).
45. Padur, S. K. R. (2021). Deep learning and process mining for ERP anomaly detection: Toward
predictive and self-monitoring enterprise platforms. Available at SSRN 5605531.
