Summary: 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.<br><br>As we move our workloads to k8s 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!<br><br>To learn more about k8s optimization best practices we have invited Stefano Doni, CTO of Akamas. Stefano walks us through key learnings as the team at Akamas has helped organizations optimize the performance, resiliency and cost of their k8s workloads. You will learn about proper memory settings, CPU throttling and how to start saving costs as you move more workloads to k8s.<br><br> <br><br>To learn more about Akamas go here: <a href="https://www.akamas.io/" rel="noopener">https://www.akamas.io/</a><br><br>If you happen to be at KubeCon 2022 in Detroit make sure to visit their booth<br><br>Show Links:<br><br>Stefano on Linkedin: <a href="https://www.linkedin.com/in/stefanodoni/" rel="noopener">https://www.linkedin.com/in/stefanodoni/</a><br><br>A Guide to Autonomous Performance Optimization with Dynatrace and Akamas: <a href="https://www.youtube.com/watch?v=i7MuEjeOvX0" rel="noopener">https://www.youtube.com/watch?v=i7MuEjeOvX0</a>
