An AI-Driven DevOps Framework for Scalable Multi-Cloud Software Deployment and Lifecycle Maintenance: An Analytical Study
Keywords:
AI-driven DevOps, multi-cloud computing, anomaly detection, cloud automationAbstract
The rapid evolution of multi-cloud computing environments has intensified the need for intelligent, adaptive, and scalable DevOps methodologies capable of handling complex software deployment and lifecycle management challenges. Traditional DevOps pipelines often struggle with heterogeneity, dynamic workload distribution, and real-time fault detection across distributed cloud infrastructures. This research proposes an AI-driven DevOps framework designed to enhance scalability, reliability, and operational efficiency in multi-cloud software deployment and maintenance. The framework integrates machine learning-based anomaly detection, predictive analytics, and causal inference mechanisms to enable autonomous monitoring, optimization, and fault remediation.
Drawing upon advancements in time-series anomaly detection (Audibert et al., 2020; Su et al., 2019), streaming analytics (Ahmad et al., 2017), and cloud operational intelligence (Chennupati, 2023), the study constructs a unified architectural model that bridges AI-based observability with DevOps automation pipelines. The proposed system incorporates hierarchical monitoring layers, real-time telemetry ingestion, and reinforcement-based deployment optimization strategies. Empirical synthesis of prior research indicates that AI-enhanced DevOps significantly improves deployment stability, reduces mean time to recovery (MTTR), and enhances cross-cloud workload balancing.
The findings suggest that integrating AI into DevOps workflows enables proactive incident detection, adaptive scaling, and improved lifecycle governance across heterogeneous cloud environments. However, challenges such as model drift, data heterogeneity, and interpretability limitations remain critical concerns. The study contributes a comprehensive analytical perspective on next-generation DevOps systems, emphasizing the convergence of AI, cloud orchestration, and autonomous system management.
Downloads
References
1. Ahmad, S., Lavin, A., Purdy, S., & Agha, Z. (2017). Unsupervised real-time anomaly detection for streaming data. Neurocomputing, 262, 134–147.
2. Artificial Intelligence and Workforce Productivity: A Comprehensive Analysis of Transformation, Opportunities, and Challenges in the Modern Workplace.” SCIENTIFIC CULTURE, 2026. https://sci-cult.net/index.php/cult/article/view/5136/3028
3. Ahmad, S., Lavin, A., Purdy, S., & Agha, Z. (2017). Unsupervised real-time anomaly detection for streaming data. Neurocomputing, 262, 134–147.
4. Audibert, J., Michiardi, P., Guyard, F., Marti, S., &Zuluaga, M. A. (2020). Usad: Unsupervised anomaly detection on multivariate time series. In Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (pp. 3395–3404).
5. Bu, J., Liu, Y., Zhang, S., Meng, W., Liu, Q., Zhu, X., & Pei, D. (2018). Rapid deployment of anomaly detection models for large number of emerging KPI streams. In 37th IEEE International Performance Computing and Communications Conference, IPCCC 2018 (pp. 1–8). IEEE.
6. Carolina, I. R. &. I. M. D. N., USA, & Tiwari, S. K. (2025b). Automation Driven Digital Transformation Blueprint: Migrating legacy QA to AI augmented pipelines. Frontiers in Emerging Artificial Intelligence and Machine Learning, 2(12), 01–20. https://doi.org/10.64917/feaiml/volume02issue12-01
7. Chen, Z., Liu, J., Gu, W., Su, Y., &Lyu, M. R. (2021). Experience report: Deep learning-based system log analysis for anomaly detection.
8. Chennupati, A. (2023). AI in cloud ops. IRE Journals, 7(5).
9. Goswami, M. J., et al. (2021). Challenges and solutions in integrating AI with multi-cloud architectures. International Journal of Enhanced Research in Management & Computer Applications.
10. Hebbar, K. S. (2025). Priority-Aware reactive APIs: Leveraging Spring WebFlux for SLA-Tiered traffic in financial services. European Journal of Electrical Engineering and Computer Science, 9(5), 31–40. https://doi.org/10.24018/ejece.2025.9.5.743
11. Huang, B., Zhang, K., Gong, M., &Glymour, C. (2019). Causal discovery and forecasting in nonstationary environments with state-space models. In Proceedings of the 36th International Conference on Machine Learning (pp. 2901–2910).
12. Kale, A. (2025). CAC Payback Period Optimization Through Automated Cohort Analysis. International Journal of Management and Business Development, 2(10), 15-20. https://doi.org/10.55640/ijmbd-v02i10-02
13. K. Bhat and G. Krishnan, "A Review of Artificial Intelligence in Finance: Driving the Next Wave of Industry Innovation," 2025 3rd International Conference on Intelligent Cyber Physical Systems and Internet of Things (ICoICI), Coimbatore, India, 2025, pp. 2033-2040, doi: 10.1109/ICoICI65217.2025.11252894.
14. Karim, A. S. A. (2025). MITIGATING ELECTROMAGNETIC INTERFERENCE IN 10G AUTOMOTIVE ETHERNET: HYPERLYNX-VALIDATED SHIELDING FOR CAMERA PCB DESIGN IN ADAS LIGHTING CONTROL. International Journal of Apllied Mathematics, 38(2s), 1257–1268. https://doi.org/10.12732/ijam.v38i2s.718
15. Lai, K., Zha, D., Xu, J., Zhao, Y., Wang, G., &.
16. Li, Z., Zhao, Y., Han, J., Su, Y., Jiao, R., Wen, X., & Pei, D. (2021). Multivariate time series anomaly detection and interpretation using hierarchical inter-metric and temporal embedding. In Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining (pp. 3220–3230).
17. Enhancing Financial Security through the Integration of Machine Learning Models for Effective Fraud Detection in Transaction Systems. (2025). Architecture Image Studies, 6(3), 531-555. https://doi.org/10.62754/ais.v6i3.248
18. Nayeem, M. 2026. Bridging Zero-Trust Security and Legacy Medical Devices: An Evaluation of Windows 11 Adoption in Hospital Clinical Workstations. Frontiers in Emerging Artificial Intelligence and Machine Learning. 3, 1 (Jan. 2026), 01–08. DOI:https://doi.org/10.64917/feaiml/Volume03Issue01-01.
19. Pham, Q., Liu, C., Sahoo, D., & Hoi, S. C. H. (2022). Learning fast and slow for online time series forecasting.
20. Pum, M. (2024). AI in multi-cloud management: Achieving seamless integration. ResearchGate.
21. Ravilla, H. (2026). Predictive Analytics for Customer Churn in Salesforce Service Cloud. In: Mishra, D., Yang, X.S., Unal, A., Jat, D.S. (eds) Data Science and Big Data Analytics. IDBA 2025. Learning and Analytics in Intelligent Systems, vol 55. Springer, Cham. https://doi.org/10.1007/978-3-032-05377-0_2
22. Ren, H., Xu, B., Wang, Y., Yi, C., Huang, C., Kou, X., Xing, T., Yang, M., Tong, J., & Zhang, Q. (2019). Time-series anomaly detection service at Microsoft. In Proceedings of the 25th ACM SIGKDD international conference on knowledge discovery & data mining (pp. 3009–3017).
23. S. R. Varanasi, "AI-Driven DevOps in Modern Software Engineering-A Review of Machine LearningBased Intelligent Automation for Deployment and Maintenance," 2025 IEEE 2nd International Conference on Information Technology, Electronics and Intelligent Communication Systems (ICITEICS), Bangalore, India, 2025, pp. 1-7, doi: 10.1109/ICITEICS64870.2025.11340882.
24. Soldani, J., &Brogi, A. (2023). Anomaly detection and failure root cause analysis in (micro) service-based cloud applications: A survey. ACM Comput. Surv., 55(3), 59:1–59:39.
25. Su, Y., Zhao, Y., Niu, C., Liu, R., Sun, W., & Pei, D. (2019). Robust anomaly detection for multivariate time series through stochastic recurrent neural network. In Proceedings of the 25th ACM SIGKDD international conference on knowledge discovery & data mining (pp. 2828–2837).
26. S. R. Varanasi, "AI-Driven DevOps in Modern Software Engineering-A Review of Machine LearningBased Intelligent Automation for Deployment and Maintenance," 2025 IEEE 2nd
27. Wu, R., & Keogh, E. (2021). Current time series anomaly detection benchmarks are flawed and are creating the illusion of progress. IEEE Transactions on Knowledge and Data Engineering.
28. Wu, X., Xiao, L., Sun, Y., Zhang, J., Ma, T., & He, L. (2022). A survey of human-in-the-loop for machine learning. Future Gener. Comput. Syst., 135, 364–381.
29. Yang, W., Zhang, K., & Hoi, S. C. (2022). Causality-based multivariate time series anomaly detection. arXiv preprint arXiv:2206.15033.
Downloads
Published
Issue
Section
License
Copyright (c) 2026 Dr. Eglė Kazlauskaitė (Author)
This work is licensed under a Creative Commons Attribution 4.0 International License.
All articles published by The Parthenon Frontiers and its associated journals are distributed under the terms of the Creative Commons Attribution (CC BY 4.0) International License unless otherwise stated.
Authors retain full copyright of their published work. By submitting their manuscript, authors agree to grant The Parthenon Frontiers a non-exclusive license to publish, archive, and distribute the article worldwide. Authors are free to:
-
Share their article on personal websites, institutional repositories, or social media platforms.
-
Reuse their content in future works, presentations, or educational materials, provided proper citation of the original publication.
