PerfectScalePerfectScale
Live Webinar · 45 min

Stop Kubernetes from Breaking Your Java Memory Management

Java applications that run perfectly in development often crash with OOM errors in Kubernetes. The culprit isn't your application code. Kubernetes resource limits directly interfere with JVM memory management, causing garbage collection failures and unexpected crashes. This technical session reveals why these conflicts happen and provides practical solutions for platform engineers managing containerized Java workloads.

Register for the Webinar

Save your spot — 45 min, live.

By submitting, you agree to our Privacy Policy.

About This Webinar

Your Java applications are running fine in development, but crashing with OOM errors in Kubernetes. The problem isn't your code. It's how Kubernetes resource limits interfere with JVM memory management, causing garbage collection failures and unexpected crashes that teams spend hours debugging. This technical webinar reveals the hidden mechanics behind Kubernetes-Java memory conflicts. You'll learn why JVM heap sizing algorithms fail in containers, how resource limits trigger performance-killing GC behavior, and practical techniques to optimize both without constant restarts. Designed for platform engineers and Java developers managing containerized workloads. Walk away with actionable strategies to eliminate OOM crashes, reduce debugging time, and optimize resource allocation for Java applications in production Kubernetes clusters.

Meet [Speaker Name]

[N

[Speaker Name]

Senior Platform Engineer

Platform engineering expert with 8+ years optimizing Java applications in Kubernetes environments. Has helped 200+ teams resolve JVM performance issues and reduce container resource waste across production clusters.

What You'll Learn

  • 1

    Why JVM defaults cause 60% of Java OOM errors in Kubernetes containers

  • 2

    How to prevent G1GC from degrading performance by 300% under memory pressure

  • 3

    Techniques to optimize Java memory without breaking warm-up cycles

  • 4

    Real-world examples of reducing Java debugging time by 15+ hours per month

  • 5

    Automated approaches to continuous right-sizing for JVM workloads