AI Driven Cloud Native Enterprise Reliability Framework for Predictive Analytics and Intelligent DevOps Automation
Abstract
Cloud-native enterprise systems have transformed modern digital infrastructures by enabling scalability, flexibility, and rapid deployment across distributed environments. However, the increasing complexity of microservices, container orchestration, and hybrid cloud ecosystems has introduced significant reliability and operational challenges. This research proposes an AI-driven cloud-native enterprise reliability framework that integrates predictive analytics with intelligent DevOps automation to improve system resilience, operational efficiency, and proactive incident management. The framework leverages machine learning algorithms, anomaly detection models, and real-time telemetry data to predict failures before they occur and automate corrective actions through intelligent orchestration mechanisms. By combining artificial intelligence with DevOps practices such as continuous integration, continuous delivery, infrastructure as code, and automated monitoring, organizations can reduce downtime, enhance service availability, and optimize resource utilization. The proposed framework also incorporates Kubernetes-based orchestration, observability platforms, and reinforcement learning techniques to support adaptive decision-making in dynamic cloud environments. Furthermore, the study evaluates the effectiveness of predictive maintenance and automated remediation in minimizing operational risks and improving enterprise reliability. The framework demonstrates how AI-powered DevOps ecosystems can support self-healing infrastructure, faster incident response, and continuous service optimization. This research contributes to the advancement of autonomous cloud operations and intelligent enterprise infrastructure management in modern digital transformation initiatives.
Article Information
Journal |
International Journal of Science, Research and Technology |
|---|---|
Volume (Issue) |
Vol. 8 No. 4 (2025): International Journal of Science, Research and Technology (IJSRAT) |
DOI |
|
Pages |
14633-14642 |
Published |
July 16, 2025 |
| Copyright |
All rights reserved |
Open Access |
This work is licensed under a Creative Commons Attribution 4.0 International License. |
How to Cite |
Ganesh Gurudu (%2025). AI Driven Cloud Native Enterprise Reliability Framework for Predictive Analytics and Intelligent DevOps Automation. International Journal of Science, Research and Technology , Vol. 8 No. 4 (2025): International Journal of Science, Research and Technology (IJSRAT) , pp. 14633-14642. https://doi.org/10.15662/IJSRAT.2025.0804008 |
References
2. Pasumarthi, H. (2023). Applying machine learning to high-volume banking platforms: From transaction data to predictive risk intelligence. International Journal of Artificial Intelligence & Machine Learning, 2(1), 356–370. https://doi.org/10.34218/IJAIML_02_01_029
3. Sengupta, J., & Alzbutas, R. (2022). Intracranial hemorrhages segmentation and features selection applying cuckoo search algorithm with gated recurrent unit. Applied Sciences, 12(21), 10851.
4. Narayanan, S. (2023). Operationalizing Artificial Intelligence Security in the Cloud: A Practical Integration framework for Enterprise Risk Management. International Journal of Future Innovative Science and Technology (IJFIST), 6(3), 10619.
5. Gopinathan, V. R. (2024). Secure explainable AI on Databricks–SAP cloud for risk-sensitive healthcare analytics and swarm-based QoS control. International Journal of Engineering & Extended Technologies Research (IJEETR), 6(4), 8452-8459.
6. Kunadi, S. K. (2024). Improving Data Quality and Deduplication Using Similarity Scoring and Confidence Models. International Journal of Computer Technology and Electronics Communication, 7(4), 9200-9211.
7. Namdeo, A. (2021). Quantum-accelerated cloud BI query optimization. International Journal of Engineering & Extended Technologies Research (IJEETR), 3(5), 3715–3724.
8. Devineni, A. (2025). Automated Remediation Guardrails: A Risk-Aware Framework for Validating AI-Generated Production Scripts in Regulated Financial Infrastructure. International Journal of AI, BigData, Computational and Management Studies, 6(2), 113-118.
9. Panyala, V. R. (2024). Designing self-healing cloud architectures for mission-critical distributed systems. International Journal of Science, Research and Technology, 7(2), 11717–11721.
10. Appani, C., & Guda, D. P. (2023). Self-supervised representation learning for zero-day attack detection in encrypted network traffic. Computer Fraud & Security, 2023(7), 20–31. Retrieved from: https://computerfraudsecurity.com/index.php/journal/article/view/661
11. Sarabu, V. B. (2024). Architecting controlled international platform rollouts: Data governance, validation, and risk mitigation in retail modernization. International Journal of Research Publications in Engineering, Technology and Management (IJRPETM), 7(1), 306–328.
12. Subramanyam, S. P. (2022). Kubernetes-oriented continuous deployment architecture for .NET microservices. International Journal of Future Innovative Science and Technology (IJFIST), 5(3), 8482–8490. https://doi.org/10.15662/IJFIST.2022.0503002
13. Mallireddy, S. (2023). Servicenow & Generative AI: Improving Infant Mortality Rate. International Journal of Computer Technology and Electronics Communication, 6(5), 1-7.
14. Adepu, R. (2024). Secure cloud migration strategies for enterprise data center modernization. International Journal of Engineering & Extended Technologies Research (IJEETR), 6(6), 239–258.
15. Kasireddy, J. R. (2025). Leveraging big data analytics for enhanced commercial vehicle safety: FMCSA's data engineering journey. International Journal of Scientific Research in Computer Science, Engineering and Information Technology, 11(2), 3203–3222. https://doi.org/10.32628/CSEIT25112796
16. Prasad, P. K. (2021). Kubernetes everywhere: Operating hybrid and multi-cloud infrastructure at scale. International Journal of Engineering & Extended Technologies Research, 3(4), 3393–3401.
17. Suvvari, S. K. (2023). Shift Left: Moving the Inclusion of Accessibility Functionalities to the Left in Agile Product Development Life Cycle. Journal of Computational Analysis and Applications, 31(4).
18. Joyce, S. (2024). Automated enterprise system reliability: Integrating AI-driven monitoring with cloud-based SAP deployment pipelines. International Journal of Research and Applied Innovations (IJRAI), 7(2), 10474–10482. https://doi.org/10.15662/IJRAI.2024.0702010
19. Soundappan, S. J. (2024). AI-Driven Customer Intelligence in Enterprise Lakehouse Systems Sentiment Mining Governance-Aware Analytics and Real-Time Data Synchronization. International Journal of Advanced Engineering Science and Information Technology (IJAESIT), 7(5), 14905.
20. Adepu, G. (2023). Intelligent digital government platforms: Leveraging machine learning and cloud architecture for social service delivery. International Journal of Computer Technology and Electronics Communication (IJCTEC), 6(3), 75–92.
21. Hossain, M. S., Hossain, M. S., Ali, M., & Rahman, M. W. (2025). Data-Driven Strategies for Predicting and Enhancing Rural Business Growth in the United States. Data-Driven Strategies for Predicting and Enhancing Rural Business Growth in the United States, 1(7), 121-146.