Generative AI–Driven Cloud Modernization and Causal Root Cause Analysis for Scalable Microservice Data Platforms
DOI:
https://doi.org/10.15662/IJEETR.2025.0703006Keywords:
Generative AI, Cloud Modernization, Root Cause Analysis, Microservices, Distributed Systems, Cloud Computing, Causal Inference, Machine Learning, Large Language Models, Predictive Analytics, Observability, Distributed Tracing, Kubernetes, Data Platforms, Self-Healing SystemsAbstract
Cloud modernization has become a strategic priority for organizations seeking scalability, agility, resilience, and operational efficiency in digital transformation initiatives. Traditional monolithic systems are increasingly being migrated toward cloud-native microservice architectures to support dynamic workloads, distributed computing, and continuous service delivery. However, modernization introduces significant challenges related to system complexity, distributed dependencies, fault diagnosis, and operational observability. Generative Artificial Intelligence (Generative AI) has emerged as a transformative technology capable of automating cloud modernization processes, improving infrastructure optimization, and enabling intelligent root cause analysis in distributed systems. This research explores a Generative AI–driven framework for cloud modernization and causal root cause analysis in scalable microservice data platforms. The proposed framework integrates machine learning, large language models, causal inference, distributed tracing, and predictive analytics to automate migration processes, monitor microservice behavior, detect anomalies, and identify the underlying causes of failures. The study emphasizes the importance of AI-powered observability and intelligent automation in improving scalability, reducing operational downtime, accelerating troubleshooting, and enhancing cloud reliability. The framework utilizes telemetry data generated from logs, metrics, traces, and infrastructure events to support proactive monitoring and self-healing mechanisms. Experimental findings indicate that Generative AI significantly enhances modernization efficiency, root cause localization accuracy, and system adaptability compared to traditional rule-based approaches. The research contributes to the development of autonomous cloud-native ecosystems capable of supporting resilient and scalable enterprise data platforms
References
1. Raja, G. V. (2023). Modernizing Enterprise Systems using AI with Machine Learning and Cloud Computing for Intelligent Systems. International Journal of Future Innovative Science and Technology (IJFIST), 6(6), 11713.
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. (2024). Causal Inference in Distributed Tracing: Automating Root Cause Analysis in Complex Microservice Dependencies. International Journal of Emerging Trends in Computer Science and Information Technology, 5(4), 166-173.
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. 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.
16. 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
17. 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.
18. 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).
19. 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
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.
