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Overprovisioning in Astro Private Cloud (APC) is a cluster-level setting that sets resource requests for selected Airflow components to a fraction of their limits. This allows the scheduler to pack more pods on nodes (bin-packing) while still allowing bursts up to the limit when needed.

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How overprovisioning works

You configure three values in your cluster’s deployment config:
  • overProvisioningFactorCPU: Fraction of the CPU limit used as the CPU request (0 < value ≤ 1).
  • overProvisioningFactorMem: Fraction of the memory limit used as the memory request (0 < value ≤ 1).
  • overProvisioningComponents: List of component names that receive the overprovisioning factor.
For each component in the list, requests are set as:
  • requests.cpu = limits.cpu × overProvisioningFactorCPU
  • requests.memory = limits.memory × overProvisioningFactorMem
For example, if a component has limits: { cpu: "2000m", memory: "4Gi" } and both factors are 0.5, then requests become { cpu: "1000m", memory: "2Gi" }. Default factors of 1 mean no change (requests equal limits).

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Configure overprovisioning

Set these keys in your cluster config (for example, in the cluster’s deployment config or the values used when registering the cluster). Values must be greater than 0 and less than or equal to 1.
Only components that have resources defined in the deployment config are affected. Components not listed in overProvisioningComponents keep their existing request/limit values.

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Supported components

You can include any of these in overProvisioningComponents:

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Choose factor values

  • 1 (default): No overprovisioning; requests equal limits. Use when you want predictable capacity and no bin-packing.
  • 0.5: Requests are half of limits. Common choice for better bin-packing while keeping headroom.
  • Lower values (for example, 0.25): More aggressive bin-packing; ensure your workloads can tolerate less guaranteed CPU/memory.
Start with 0.5 for CPU and memory and adjust based on utilization and scheduling behavior.

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Best practices

  • Apply factors per cluster: Overprovisioning applies at the cluster level; all deployments on the cluster use the same factors for the listed components.
  • Include only components that have resources: Only components with resources.limits (and optionally resources.requests) in the deployment config receive modifications; listing others has no effect.
  • Monitor utilization: After enabling, watch pod scheduling and resource usage to confirm the factors match your workload and node capacity.