Predictive Performance Modeling for SAP Systems and HANA Database: Leveraging Job History, Usage Patterns and Database Metrics for Proactive Optimization

Kolluri, Srinivas (2025) Predictive Performance Modeling for SAP Systems and HANA Database: Leveraging Job History, Usage Patterns and Database Metrics for Proactive Optimization. International Journal of Science and Research Archive, 16 (2). 001-007. ISSN 2582-8185

SAP systems, particularly those running on SAP HANA, face unique challenges in performance optimization due to their real-time, in-memory processing nature. The predictive performance modeling framework incorporates both SAP job history and SAP HANA database metrics to forecast system performance and preemptively address potential issues. By leveraging machine learning algorithms and analyzing historical performance trends, the model predicts bottlenecks, database slowdowns, and job failures, enabling proactive capacity planning and tuning. Real-world implementations demonstrate how predictive analytics optimizes performance across SAP applications and the HANA database, highlighting improvements in both database and application layer performance. Additionally, integration with cloud-native SAP environments enables dynamic resource scaling based on predictions to minimize operational disruptions and ensure high availability.