Rightsizing JVM workloads on Kubernetes requires knowing what the appropriate heap size should be and then sizing the container around that heap size. Other rightsizing tools don’t dive deep enough into the JVM, so their recommendations are based solely on memory usage at the container level. StormForge’s Java Workload Optimization brings in JVM metrics and allows holistic rightsizing of JVM workloads.
By analyzing Java metrics using machine learning, StormForge generates tailored recommendations for heap size, as well as Kubernetes resource requests and limits. These recommendations can be reviewed and applied through a user-friendly interface, or they can be deployed automatically, for continuous performance optimization.
