Oracle OIPA Cloud Migration Analysis: Machine Learning Models for Predicting Resource Utilization and Success Outcomes

This study examines Oracle Insurance Policy Administration (OIPA) Coud Migration projects, analyzing 30 implementations that migrated from SQL Server to Oracle Cloud Infrastructure (OCI) environments. The research focuses on Universal Life Insurance systems migrating from AWS-hosted environments to Oracle’s cloud platform, including site upgrades from version 11.2 to 11.3.x. The migration strategy emphasizes minimal architectural changes while achieving improved performance, security, and scalability outcomes. Data analysis reveals significant relationships between input variables including infrastructure costs ($36.4k-$63.5k), migration timeline (9-19 weeks), data sizes (1.6-4.2TB), and code complexity scores (scales 2-7), which are correlated with output metrics of resource utilization (65-81%) and success scores (73-91%). There are strong positive correlations among complexity factors, while inverse relationships emerge between complexity and performance outcomes. Machine learning models were evaluated to predict resource utilization, with random forest regression showing severe overfitting (training R²=0.9674, testing R²=0.5890) and support vector regression