(untitled) (International Journal of Engineering & Extended Technologies Research (IJEETR), 2025)
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