Over the years we learned how to optimize the performance of our JVMs, our CLRs or our databases instances by tweaking settings around heap sizes, garbage collection behavior or connection and thread pools.
As we move our workloads to Kubernetes we need to adapt our optimization efforts as they are new nobs to turn. We need to factor in how resource and request limits on pods impact your application runtimes that run on your clusters. Out of memory problems are all of a sudden no longer just depending on the java heap size alone!
To learn more about Kubernetes optimization best practices Andy Grabner and Brian Wilson of Dynatrace have invited Stefano Doni, CTO of Akamas, to join their PurePerformance podcast.
Stefano walks us through key learnings as the team at Akamas has helped organizations optimize the performance, resiliency and cost of their Kubernetes workloads. You will learn about proper memory settings, CPU throttling and how to start saving costs as you move more workloads to Kubernetes.
Experience the benefits of Akamas autonomous optimization.
No overselling, no strings attached, no commitments.