What Should I Do for Repeated Scale-Out During Node Provisioning?
Symptom
When unscheduled pods have heterogeneous resource requirements, Autoscaler may over-provision nodes due to resource fragmentation. A second scale-out can be triggered while nodes from the first are still being provisioned.
Possible Cause
- Initial calculation: During the first scale-out, Autoscaler uses a greedy algorithm to compute the theoretical minimum nodes required.
- Node creation: After the first scale-out is triggered, the pending pods remain unschedulable during node creation.
- Recalculation: Autoscaler reevaluates scale-out requirements, simulating pod placement onto pending nodes from the initial scale-out via the integrated scheduler framework.
- Fragmentation: The scheduler's node-selection logic (based on resource utilization, among other factors) may fragment resources across the new nodes. Consequently, the initially provisioned nodes cannot accommodate the full batch of pods, forcing additional scale-out.
Solution
This behavior is expected based on the Autoscaler's design and does not require intervention.
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