It watches for pods that cannot be scheduled and adds or removes nodes to the cluster to accommodate those pods.Ī key feature of Kubernetes that enables both of these scaling actions is the capability to set specific resource requests and limits on your workloads. Cluster Autoscaler, meanwhile, handles scaling of the cluster itself. HPAs monitor a target metric of individual pods within a deployment (often CPU or memory usage), and they add or remove pods as necessary to keep that metric near a specified target. Each individual Kubernetes deployment can be scaled automatically using a Horizontal Pod Autoscaler (HPA), while the cluster at large is scaled using Cluster Autoscaler. Kubernetes is a dynamic system that automatically adapts to your workload’s resource utilization. In this blog, I’ll talk about Kubernetes best practices for correctly setting resource requests and limits. I was so frustrated by this that I created Goldilocks, an open source project, to make the process of setting initial resource requests and limits easier. One of my biggest pet peeves when managing Kubernetes is when there are workloads with no resource requests and limits. Guest post originally published on Fairwinds’s blog by Andy Suderman, Lead R&D engineer at Fairwinds
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