Resource inefficiency with K8S autoscaling often begins with improper vertical scaling. Then horizontal scaling compounds these issues, which manifest as high cloud costs as the cluster autoscaler adds instances. In this talk, I will inform people how they can use ML to enable effective autoscaling.

Without autoscaling, most companies recognise they’re either wasting a lot of resources or risking performance/reliability issues. There’s no way to effectively set resource requests unless your actual usage is completely flat. A way to solve this is by having knowledgeable people look at it all day to make adjustments, or you can just take the financial hit or the risk of instability. Alternatively one can use technology like machine learning to solve the issue with high accuracy and little to no effort. In this talk, I will inform people how they can use machine learning to enable effective autoscaling.

Erwin Daria is Director – Alliances and Partnerships at StormForge. After serving in roles building and leading infrastructure teams, Erwin has transitioned to the vendor side, serving and finding success in sales, marketing and product roles for companies like Tintri and Juniper Networks.