Optimizing Java Virtual Machine (JVM) performance can significantly enhance Java applications. This research explores automating JVM tuning using machine learning (ML) models trained on garbage collection (GC) logs. The study demonstrated that ML could contribute to a 20% improvement in application throughput by adjusting JVM memory-related flags, specifically focusing on young generation size. The promising results suggest ML can simplify and improve JVM tuning even for those without in-depth JVM knowledge.
Table of contents
JVM Tuning with Machine Learning on Garbage Collection LogsJVM BasicsThe Problem and MotivationThe ApproachKey FindingsConclusion and Future WorkSort: