Enhancing Cloud-Native CI/CD Pipelines with AI-Driven Automation and Predictive Analytics
Keywords:
AI-driven automation, predictive analyticsAbstract
Adoption of cloud-native application deployment and management architecture might expedite CI/CD. Automation and intelligence enable to raise complicated system dependability and performance. Driven by artificial intelligence, predictive analytics in cloud-native CI/CD systems might enhance enterprise-scale application deployment, error prediction, and downtime.
Beginning with cloud-native designs, CI/CD pipelines, and AI's increasing influence in operations and software development, the article follows ML systems may enhance CI/CD code integration, testing, deployment, and monitoring as well as other areas. Predictive analytics may uncover security concerns, performance bottlenecks, and deployment issues compromising cloud-native application stability and performance according to research.
References
J. Humble and D. Farley, Continuous Delivery: Reliable Software Releases through Build, Test, and Deployment Automation, 1st ed. Boston, MA: Addison-Wesley, 2010.
P. C. Clements, Software Architecture in Practice, 3rd ed. Boston, MA: Addison-Wesley, 2016.
K. Beck et al., Test-Driven Development: By Example, Boston, MA: Addison-Wesley, 2002.
G. H. McAllister and A. Arvind, "Machine Learning for DevOps: A Survey of Techniques," ACM Computing Surveys, vol. 53, no. 4, pp. 1-37, Dec. 2020.
M. M. D. D. A. Rahman, "Predictive Analytics in DevOps: A Review," International Journal of Computer Applications, vol. 174, no. 3, pp. 18-26, Oct. 2017.
S. M. Al-Kahtani et al., "AI-Driven Automation for Continuous Integration and Delivery," IEEE Access, vol. 8, pp. 22938-22948, 2020.
D. S. Rosenberg and P. H. D. K. Givens, Practical Cloud Security: A Guide for Secure Design and Deployment, 1st ed. New York, NY: O'Reilly Media, 2012.
C. L. Finkel and J. S. Almeida, "Enhancing CI/CD Pipelines with Predictive Analytics," Proceedings of the 2020 IEEE International Conference on Cloud Computing Technology and Science, pp. 302-309, Dec. 2020.
A. S. Maynard et al., "Automating Deployment Processes Using Machine Learning," IEEE Transactions on Software Engineering, vol. 47, no. 5, pp. 1045-1059, May 2021.
T. L. Williams and A. R. B. Jones, "AI Techniques for Resource Management in Cloud-Native Environments," ACM Transactions on Internet Technology, vol. 20, no. 2, pp. 1-23, Mar. 2021.
R. G. McDaniel and P. V. Patel, "Exploring Predictive Models for CI/CD Failures," IEEE Transactions on Network and Service Management, vol. 17, no. 1, pp. 1-14, Mar. 2020.
J. A. Lee and K. H. Zhu, "Resource Allocation and Scaling Using Machine Learning," IEEE Transactions on Cloud Computing, vol. 9, no. 1, pp. 46-59, Jan.-Mar. 2021.
M. A. Sanchez and N. W. Hawkins, "AI-Enhanced Code Quality Monitoring and Management," Proceedings of the 2019 IEEE/ACM International Conference on Automated Software Engineering, pp. 68-77, Sep. 2019.
E. M. Collins et al., "Challenges and Best Practices in AI-Driven CI/CD Automation," Journal of Software: Evolution and Process, vol. 32, no. 4, pp. 1-15, Apr. 2020.
H. K. Klein and B. M. McKeen, "Interpretability in Machine Learning Models for CI/CD," Journal of Computing and Information Technology, vol. 28, no. 1, pp. 19-35, Mar. 2020.
J. P. Black and M. A. Roberts, "Managing Data for AI-Driven Automation in DevOps," IEEE Cloud Computing, vol. 7, no. 2, pp. 56-65, Mar.-Apr. 2020.
L. D. Lee and T. W. Griffiths, "Bias and Risk Management in AI Models for DevOps," IEEE Transactions on Artificial Intelligence, vol. 1, no. 3, pp. 223-234, Jun. 2020.
M. D. Davis and J. E. Baker, "Automated Testing Frameworks Enhanced by AI," IEEE Software, vol. 37, no. 1, pp. 42-50, Jan.-Feb. 2020.
T. A. Curtis et al., "Case Studies in AI-Driven CI/CD Pipeline Optimization," Proceedings of the 2019 IEEE International Conference on Software Maintenance and Evolution, pp. 220-229, Sep. 2019.
D. L. Robinson and A. T. Adams, "Future Directions in AI for Cloud-Native CI/CD Pipelines," IEEE Future Directions in Computing, vol. 7, no. 1, pp. 99-110, Jan. 2021.
Downloads
Published
Issue
Section
License
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
