Ada Boost Regression-Based Modelling of Heap Behaviour for Predictable Performance in Java Applications

Authors

• Tirumala Rao Gundala Consulting Technical Manager & Performance Architect, Oracle, United States

Abstract

This research addresses the critical challenge in modelling heap behaviour for predictable performance in Java applications using machine learning approaches. Since heap memory management significantly impacts application responsiveness and performance, understanding the complex relationships between heap load, garbage collection pause times, throughput, and memory usage becomes essential for optimization. Through extensive statistical analysis, this study establishes strong correlations between parameters and employs Ada Boost Regression to predict three key performance metrics across 100 observations. This method demonstrates exceptional predictive accuracy, achieving R² values ​​of 0.987, 0.955, and 0.977 for GC pause time, throughput, and memory usage respectively on the training data, with robust generalization to the test data (R² values ​​of 0.956, 0.880, and 0.937). The results reveal near-perfect positive correlations between heap load, GC pause times, and memory usage, while throughput exhibits a strong negative correlation, illustrating the fundamental JVM memory management trade-offs. This research provides practitioners with a reliable predictive tool for ensuring stable operations in production environments where performance optimization, capacity planning, and efficient memory management are paramount.