ComputeClass: Cost Optimization & FlexCUDs

Aligning with Committed Use Discounts (CUDs)

Before selecting machine families for your priorities[] list, you must identify your existing Committed Use Discounts (CUDs) and Reservations.

Key Strategy: The On-Demand "floor" of your ComputeClass should heavily bias toward machine families covered by your CUDs (Resource-based or Flexible).

FlexCUD Coverage

• Eligible: Most general-purpose and compute-optimized families (e.g., N2, N4, C3, C4, E2, N2D).
• Ineligible / Excluded:
  • GPUs
  • TPUs
  • Local SSDs
  • Memory-optimized families (M series) (typically require resource-based CUDs)
  • Preemptible / Spot VMs
  • Sole-tenant nodes

Priority List Design

When designing the priorities[] array for workloads that don't strictly require specialized hardware:

1. Spot Tier (Highest Priority): Attempt to provision Spot VMs first. Spot is cheaper than FlexCUD On-Demand but is not covered by CUDs. Use priorityScore to tie-break across multiple Spot families based on unit cost. (Note: You can assign the same score to a maximum of 3 rules).
2. FlexCUD Tier (Middle Priority / Floor): If Spot is unavailable, fall back to On-Demand families that are explicitly covered by your active FlexCUDs.
3. General On-Demand Tier (Lowest Priority - Optional): If FlexCUD families are exhausted, fall back to other general-purpose On-Demand families (e.g., E2) to ensure obtainability.

Example: Balancing Spot and FlexCUDs

  priorities:
  # 1. Try Spot first across modern families
  - machineFamily: n4
    spot: true
    priorityScore: 100
  - machineFamily: c4
    spot: true
    priorityScore: 90
  # 2. Fallback to On-Demand covered by FlexCUD
  - machineFamily: n4
    spot: false
    # Assume N4 is covered by our regional FlexCUD commit

Active Migration for Cost

Enable activeMigration to allow GKE to continuously move workloads to more cost-effective nodes as capacity becomes available.

  activeMigration:
    optimizeRulePriority: true

• If a workload falls back to an On-Demand node (because Spot was unavailable), active migration will automatically evict the pod and move it to a Spot node when Spot capacity returns.
• Throttling Vol. Disruptions: Active migration honors Pod Disruption Budgets (PDBs). Use PDBs to throttle eviction rates. To stop active migration for specific pods, add the cluster-autoscaler.kubernetes.io/safe-to-evict: "false" annotation.
• WARNING: PDBs and safe-to-evict only block voluntary scaler actions. They cannot block involuntary Spot VM preemptions.

Balanced HA Scale-Up Across Zones

"Balanced" spans two independent layers — clarify which the user wants:

• Infrastructure (nodes): locationPolicy: BALANCED spreads node scale-up roughly evenly across zones (best-effort; still scales up if a zone is short; ANY packs one zone).
• Workload (pods): BALANCED does not balance pods. Add pod topologySpreadConstraints (maxSkew:1, topologyKey: topology.kubernetes.io/zone, whenUnsatisfiable: DoNotSchedule — default ScheduleAnyway won't enforce it) on the workload (see gke-cluster-autoscaler).

Trap: one priority per zone does NOT balance — priorities are sequential, so zone-a drains fully before zone-b is tried.

Method A — locationPolicy: BALANCED (no priorityScore, works pre-1.35.2):

• Use ONE priorities[] entry per machine size (not one priority per zone). Inside it, set reservations.affinity: Specific; the reservations.specific[] list holds one entry per zone (3 zones → 3 named specific[] entries with their own name + zones). Don't make a separate priority per reservation, and don't collapse all zones into a single entry.
• Set location.locationPolicy: BALANCED. Schema: do not put location.zones on a Specific-reservation priority (error: location config with specific reservations enabled) — zones come from the reservations; the location block keeps locationPolicy only.
• Asset: balanced-reserved-zonal-compute-class.yaml.

Method B — equal priorityScore round-robin (GKE 1.35.2-gke.1842000+):

• Define separate per-zone priority rules and assign them an equal priorityScore (max 3 rules/score).
• GKE evaluates them together and round-robins for rough balance.