Articles Open Access
Autonomous DevOps Pipelines using Machine Learning-Based Deployment Optimization and Failure Prediction Techniques
Department of I.T., SNIST, Hyderabad, India
Abstract
Modern software engineering practices increasingly rely on DevOps pipelines to enable continuous integration, continuous delivery (CI/CD), and rapid deployment cycles. However, as systems scale in complexity and distributed architectures become more prevalent, traditional DevOps pipelines face challenges such as deployment failures, inefficient resource utilization, rollback overheads, and unpredictable performance degradation
This research proposes an autonomous DevOps pipeline framework enhanced with machine learning-based deployment optimization and failure prediction techniques. The framework leverages historical deployment data, system logs, performance metrics, and infrastructure telemetry to predict deployment risks and optimize release strategies in real time
Machine learning models such as gradient boosting, recurrent neural networks, and anomaly detection algorithms are integrated into the CI/CD pipeline to forecast deployment success probabilities and detect early failure signals. Additionally, reinforcement learning techniques are used to dynamically optimize deployment strategies such as canary releases, blue-green deployments, and rolling updates
The system enables self-adaptive pipeline behavior, where deployment decisions are automatically adjusted based on predictive insights. This reduces downtime, improves system reliability, and enhances deployment efficiency. The proposed approach demonstrates how AI-driven automation can transform traditional DevOps pipelines into intelligent, self-healing, and autonomous systems capable of continuous optimization
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Publication Information
| Journal | International Research Journal of Innovative Engineering |
|---|---|
| Volume | 7 |
| Number | 5 |
| Pages | 13188-13194 |
| Published | 2023-09-09 |
| Issue | Vol. 7 No. 5 (2023): International Research Journal of Innovative Engineering (IRJIE) |
References
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33. Jayaraman, S., Rajendran, S., & P, S. P. (2019). Fuzzy c-means clustering and elliptic curve cryptography using privacy preserving in cloud. International Journal of Business Intelligence and Data Mining, 15(3), 273-287.
34. Brijet, Z., & Bharathi, N. (2021). Design of type-2 fuzzy logic controller for fluid catalytic cracking unit. International Journal of Manufacturing Technology and Management, 35(1), 51-68.
35. Sudhan, S. K. H. H., & Kumar, S. S. (2015). An innovative proposal for secure cloud authentication using encrypted biometric authentication scheme. Indian Journal of Science and Technology, 8(35), 1-5.
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37. Sulthana, A. R., & Murugeswari, B. (2011, March). ARIPSO: Association rule interactive postmining using schemas and ontologies. In 2011 International Conference on Emerging Trends in Electrical and Computer Technology (pp. 941-946). IEEE.
38. Chiranjeevi, K. G., Latha, R., & Kumar, S. S. (2016). Enlarge Storing Concept in an Efficient Handoff Allocation during Travel by Time Based Algorithm. Indian Journal of Science and Technology, 9, 40.
39. Priya, P. S., & Sugumar, R. (2014). Multi Keyword Searching Techniques over Encrypted Cloud Data. In IJSR.
40. Suresh, T., Brijet, Z., & Sheeba, T. B. (2021). CMVHHO-DKMLC: A Chaotic Multi Verse Harris Hawks optimization (CMV-HHO) algorithm based deep kernel optimized machine learning classifier for medical diagnosis. Biomedical Signal Processing and Control, 70, 103034
2. Chen, L., & Zhao, L. (2020). Machine learning for DevOps automation: A survey. IEEE Access, 8, 176842–176859.
3. Debroy, V., & Wong, W. E. (2018). Machine learning for software reliability prediction. Journal of Systems and Software, 137, 36–48.
4. Humble, J., & Farley, D. (2010). Continuous delivery: Reliable software releases through build, test, and deployment automation. Addison-Wesley.
5. Kim, G., Humble, J., Debois, P., & Willis, J. (2016). The DevOps handbook. IT Revolution Press.
6. Leitner, P., & Cito, J. (2016). Patterns in the chaos—A study of performance variation in cloud applications. ACM Transactions on Internet Technology, 16(2), 1–23.
7. Mao, M., Humphrey, M., & Figueiredo, R. (2019). Cloud auto-scaling with machine learning. Future Generation Computer Systems, 93, 364–377.
8. Shahin, M., Babar, M. A., & Zhu, L. (2017). Continuous integration, delivery and deployment: A systematic review. Journal of Systems and Software, 123, 243–270.
9. Sundararajan, M., & Najafabadi, M. M. (2021). AI-driven DevOps: Opportunities and challenges. IEEE Cloud Computing, 8(4), 42–50.
10. Thung, F., Kochhar, P. S., & Lo, D. (2018). Bug report analysis using machine learning. Empirical Software Engineering, 23(3), 1234–1265.
11. Zhang, Y., & Harman, M. (2020). Search-based software engineering in DevOps. IEEE Transactions on Software Engineering, 46(12), 123–136.
12. Zhou, X., Jin, Z., & Zhang, Y. (2021). Predictive analytics in DevOps pipelines. Information and Software Technology, 136, 106566.
13. Mathew, A., & Nair, A., & Decouth, J. R. (2019). Audience Behaviour Mining Using Data Analysis. International Journal of Data Structures, 5(2), 1-7.
14. Sudarsan, V., & Sugumar, R. (2018). Building a Distributed K-Means Model using Simple K-Means of Weka.
15. Murugeshwari, B., Jayakumar, C., & Sarukesi, K. (2012). Secure Multi Party Computation Technique for Classification Rule Sharing. International Journal of Computer Applications, 55(7).
16. Mathew, A. R. (2019). Cyber-infrastructure connections and smart gird security. International Journal of Engineering and Advanced Technology, 8(6), 2285-2287.
17. Sugumar, R., & Murugeshwari, B. (2016). An Efficient MChord based Authentication for Vehicular Ad-Hoc Networks.
18. Sudhan, S. K. H. H., & Kumar, S. S. (2016). Gallant Use of Cloud by a Novel Framework of Encrypted Biometric Authentication and Multi Level Data Protection. Indian Journal of Science and Technology, 9, 44.
19. Sruthi, R. S., Ananya, S., & Murugeshwari, B. (2010). Web Based Virtual Control System Laboratory and On-Line Temperature Control of Electrophoresis Equipment using LabVIEW. International Journal of Computer Applications, 975, 8887.
20. Mathew, A. (2019). Cybersecurity infrastructure and security automation. Adv Comput: Int J (ACIJ), 10(6).
21. Sugumar, R. (2018). Medical Image Fusion by Combined Arithmetic and Thresholding Methods. EDITORS OF SPECIAL ISSUE JOURNAL, 17.
22. Balasubramanian, V., & Rajendran, S. (2019). Rough set theory-based feature selection and FGA-NN classifier for medical data classification. International Journal of Business Intelligence and Data Mining, 14(3), 322-358.
23. Brijet, Z., Kumar, B. S., & Bharathi, N. (2017). Vehicle anti-theft system using fingerprint recognition technique. Open Academic Journal of Advanced Science and Technology, 1(1), 36-41.
24. Mathew, A. R. Malware Analysis of API Calls using FPGA Hardware Level Security.
25. Sugumar, R. (2014). A technique to stock market prediction using fuzzy clustering and artificial neural networks.
26. Mathew, A., & Mai, C. (2018, May). Study of Various Data Recovery and Data Back Up Techniques in Cloud Computing & Their Comparison. In 2018 3rd IEEE International Conference on Recent Trends in Electronics, Information & Communication Technology (RTEICT) (pp. 2021-2024). IEEE.
27. Murugeshwari, B., Sudharson, K., Panimalar, S. P., Shanmugapriya, M., & Abinaya, M. (2020). SAFE–Secure Authentication in Federated Environment using CEG Key code.
28. Z. Brijet, N. Bharathi, G. Shanmugapriya. (2015). Design of Model Predictive Controller for Fluid Catalytic Cracking Unit. Fluid-IJCTA, 8(5), 1917-1926.
29. Singh, T. J., & Sugumar, R. (2012, December). An extensibility of THEMIS billing system for the cloud computing environment. In 2012 Fourth International Conference on Advanced Computing (ICoAC) (pp. 1-6). IEEE.
30. Mathew, A. (2020). Threat intelligence and internet of medical things (IoMT). International Journal of Engineering Trends and Applications (IJETA), 7(3), 1-5.
31. Amutha, M., & Sugumar, R. (2015). A survey on dynamic data replication system in cloud computing. International Journal of Innovative Research in Science, Engineering and Technology, 4(4), 1454-1467.
32. Sudarsan, V., & Sugumar, R. (2019). Building a distributed K‐Means model for Weka using remote method invocation (RMI) feature of Java. Concurrency and Computation: Practice and Experience, 31(14), e5313.
33. Jayaraman, S., Rajendran, S., & P, S. P. (2019). Fuzzy c-means clustering and elliptic curve cryptography using privacy preserving in cloud. International Journal of Business Intelligence and Data Mining, 15(3), 273-287.
34. Brijet, Z., & Bharathi, N. (2021). Design of type-2 fuzzy logic controller for fluid catalytic cracking unit. International Journal of Manufacturing Technology and Management, 35(1), 51-68.
35. Sudhan, S. K. H. H., & Kumar, S. S. (2015). An innovative proposal for secure cloud authentication using encrypted biometric authentication scheme. Indian Journal of Science and Technology, 8(35), 1-5.
36. Mathew, A. R. Airport Cyber Security and Cyber Resilience Controls. arXiv 2019. arXiv preprint arXiv:1908.09894.
37. Sulthana, A. R., & Murugeswari, B. (2011, March). ARIPSO: Association rule interactive postmining using schemas and ontologies. In 2011 International Conference on Emerging Trends in Electrical and Computer Technology (pp. 941-946). IEEE.
38. Chiranjeevi, K. G., Latha, R., & Kumar, S. S. (2016). Enlarge Storing Concept in an Efficient Handoff Allocation during Travel by Time Based Algorithm. Indian Journal of Science and Technology, 9, 40.
39. Priya, P. S., & Sugumar, R. (2014). Multi Keyword Searching Techniques over Encrypted Cloud Data. In IJSR.
40. Suresh, T., Brijet, Z., & Sheeba, T. B. (2021). CMVHHO-DKMLC: A Chaotic Multi Verse Harris Hawks optimization (CMV-HHO) algorithm based deep kernel optimized machine learning classifier for medical diagnosis. Biomedical Signal Processing and Control, 70, 103034
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