Cluster Autoscaler: Optimization Profiles & Location Policies

Autoscaling Profiles (Cluster-wide)

• Command: gcloud container clusters update <C> --autoscaling-profile=optimize-utilization.

Spot Best Practices

• Mandatory Fallback: Any time you recommend or debug the use of Spot instances (whether via manual pools or node pool auto-creation), suggest including other Spot or On-Demand fallback priorities via ComputeClasses. Spot capacity is highly variable, and without a fallback, workloads will become stuck during GCE stockouts (scale.up.error.out.of.resources).

Location Policies (--location-policy)

• BALANCED: Best-effort even node spread across zones at scale-up (infrastructure layer; still scales up if a zone is short). Does not balance pods — that's a separate workload layer needing pod topologySpreadConstraints (DoNotSchedule, see PTS below). The two are independent. Use for HA workloads.
• ANY: Grabs capacity from any zone. Best for Spot VMs and scarce SKUs (maximizes obtainability).

ComputeClass locationPolicy

priorities:
- machineFamily: n4
  spot: true
  location:
    locationPolicy: ANY # Spot preference

Pod Topology Spread Constraints (PTS)

Cluster Autoscaler supports PTS for zonal spreading during scale-up.

To enforce spreading via autoscaler, you MUST use whenUnsatisfiable: DoNotSchedule.

Example Configuration:

spec:
  topologySpreadConstraints:
  - maxSkew: 1
    topologyKey: "topology.kubernetes.io/zone"
    whenUnsatisfiable: DoNotSchedule  # Required for cluster autoscaler compatibility
    labelSelector:
      matchLabels:
        app: my-app

Resource CUDs vs. Reservations

• Committed Use Discounts (CUDs): Automatically consumed by the Cluster Autoscaler. When the autoscaler provisions a node of a specific machine family (e.g., n4), it automatically consumes any available CUD for that family up to exhaustion. No explicit autoscaler, Node Auto Provisioning, or ComputeClass configuration is needed.
• Reservations: Unlike CUDs, capacity reservations are not automatically consumed. They must be explicitly targeted. You must configure consumption via the Node Pool API (for standard/manual pools) or via a ComputeClass reservations block (for node pool auto-creation).
• Freshly-created reservations (cache lag): The autoscaler caches reservation data and does not see a new reservation immediately. Driving scale-up against a brand-new reservation while Cluster Autoscaler's cache is stale makes Cluster Autoscaler fail to find the capacity and back off that reservation — which delays retries and compounds the stall. Fix: wait at least 30 minutes after creating a reservation before relying on it for autoscaler-driven scale-up. (Applies to the reservation itself; growing an existing, already-cached reservation is fine.)