Charla: The efficient way of autoscaling your workload in kubernetes

The Efficient Way of Autoscaling Your Workload in Kubernetes

Have you faced limited access to observability data for Horizontal Pod Autoscaler (HPA) or dealt with provisioning resources that result in unnecessary costs? Organizations often create autoscaling rules based on CPU and memory usage, but guaranteeing reliability and cost efficiency solely with CPU can be challenging in a Kubernetes environment. How can we effectively leverage observability data in autoscaling rules?

Discover Keptn's Metric Server, addressing these challenges by consolidating observability data in Kubernetes. Join our presentation to see how the Keptn Metrics Server effortlessly scrapes metrics, facilitating autoscaling with HorizontalPodAutoscaler. Through a real example showcasing inefficient HPA rules, we will explain how it can enhance and optimize the autoscaling process utilizing metrics from Istio/Envoy, but also the cost of your workload.
Come and learn the best practices to define the right Autoscaling policies.

Bio:
Henrik is a Cloud Native Advocate at Dynatrace, the leading Observability platform. Prior to Dynatrace, Henrik has worked more than 15 years, as Performance Engineer. Henrik Rexed Is Also one of the Organizer of the conferences named WOPR, KCD Austria and the owner of the Youtube Channel IsitObservable.