AI-Driven Hybrid Strategies for Cloud Migration and Modernization of Java Applications

The modernization of enterprise Java applications is increasingly critical as organizations transition from monolithic architectures to cloud-native environments.Hybrid cloud strategies, supported by artificial intelligence (AI), enable a balanced approach that leverages the scalability of public cloud while preserving the control of on-premises infrastructure.This paper examines AI-driven methodologies that support each phase of migration, including discovery, decomposition, deployment planning, resource allocation, automation, and monitoring.Techniques such as microservices decomposition using clustering algorithms, reinforcement learning for dynamic autoscaling, and AI-assisted DevOps pipelines are analyzed in detail.Additionally, the role of Red Hat OpenShift Service on AWS (ROSA) is highlighted as a managed hybrid solution for migrating Java services to AWS without the need for native re-engineering.The study consolidates prior research and industry practices to provide a structured framework for AI-enhanced hybrid migration and modernization of Java applications, offering insights into benefits, challenges, and future research opportunities.