Auto Mode

Auto mode is the production end-state for attune. The operator continuously resizes all eligible pods based on observed metrics. Before enabling Auto mode, you should have validated recommendations through Recommend and/or Canary mode.

Prerequisites

Before switching to Auto mode:

1. Run in Recommend mode for at least 1 full history window (default 7 days) to build confidence in the recommendations
2. Verify recommendations are reasonable using the kubectl plugin:

   kubectl attune recommendations -n <namespace>
   

3. Test with Canary mode (optional but recommended) to validate resizes on a subset of pods before the full fleet
4. Configure appropriate bounds to prevent extreme recommendations:

   cpu:
     minAllowed: "50m"    # never go below 50 millicores
     maxAllowed: "4000m"  # never exceed 4 cores
   memory:
     minAllowed: "64Mi"   # never go below 64 MiB
     maxAllowed: "8Gi"    # never exceed 8 GiB
   

Creating an Auto-mode policy

apiVersion: attune.io/v1alpha1
kind: AttunePolicy
metadata:
  name: my-app
  namespace: production
spec:
  targetRef:
    kind: Deployment
    selector:
      matchLabels:
        tier: api
  metricsSource:
    prometheus:
      address: http://prometheus-server.monitoring:80
    historyWindow: 168h  # 7 days of data
  cpu:
    percentile: 95
    overhead: "20"
    minAllowed: "50m"
    maxAllowed: "4000m"
    controlledValues: RequestsAndLimits
  memory:
    percentile: 99
    overhead: "30"
    minAllowed: "64Mi"
    maxAllowed: "8Gi"
    controlledValues: RequestsAndLimits
  updateStrategy:
    type: Auto
    cooldown: 1h
    autoRevert: true

Recommended guardrails

Setting	Purpose	Suggested value
overhead	Headroom above observed usage	20% (CPU), 30% (memory)
minAllowed/maxAllowed	Prevent extreme recommendations	Match your resource limits policy
cooldown	Time between resizes	1h minimum for production
autoRevert	Roll back if pods become unhealthy	true for production

The safety monitor watches each resized pod for an observation period before declaring the resize successful. The default is 5 minutes. To configure it, set safetyObservationPeriod:

spec:
  updateStrategy:
    type: Auto
    autoRevert: true
    safetyObservationPeriod: 10m  # safety watch window after each resize

Overhead guidance

• CPU: 20% overhead works well for steady-state services. Use 50% for bursty workloads.
• Memory: 30% overhead is recommended because memory pressure causes OOM kills. Never go below 10% for production.

Monitoring Auto mode

Check policy status

# Overview of all policies
kubectl attune status -A

# Estimated savings
kubectl attune savings -n production

# Detailed per-container recommendations
kubectl attune recommendations -n production

Watch for degradation

The operator sets a Degraded condition when 3 or more of the last 5 resizes are reverted. Monitor this with:

kubectl get rsp -A -o jsonpath='{range .items[*]}{.metadata.namespace}/{.metadata.name}: {range .status.conditions[*]}{.type}={.reason} {end}{"\n"}{end}'

Prometheus metrics

The operator exports metrics for dashboarding:

• attune_recommendation_cpu_cores -- Recommended CPU per workload
• attune_recommendation_memory_bytes -- Recommended memory per workload
• attune_confidence -- Confidence score (0-1) per workload
• attune_resize_total -- Total successful, failed, and reverted in-place resize operations
• attune_eviction_total -- Total eviction fallback attempts when resizeMethod: InPlaceOrRecreate
• attune_reverts_total -- Total reverts (broken down by reason)

Alert on high revert rates:

- alert: AttuneHighRevertRate
  expr: rate(attune_reverts_total[1h]) > 0.1
  for: 10m
  annotations:
    summary: "High revert rate for {{ $labels.namespace }}/{{ $labels.workload }}"

Scheduled resizes

By default, resizes can occur at any time. Use the schedule field to restrict resizes to specific time windows and days of the week. Recommendations are always computed; only the actual resize execution is gated.

spec:
  updateStrategy:
    type: Auto
    schedule:
      windows:
        - start: "02:00"
          end: "06:00"
      daysOfWeek: ["Monday", "Tuesday", "Wednesday", "Thursday", "Friday"]
      timezone: "America/New_York"

Key behavior:

• If daysOfWeek is omitted, all days are allowed.
• If windows is omitted, all times are allowed (only day filtering applies).
• Overnight windows work: start: "22:00", end: "06:00" wraps past midnight.
• The ScheduleBlocked status condition is set when outside the window.
• An invalid timezone name fails open (resizes are allowed) to prevent silent lockout from a typo.

Combine scheduling with budget caps for large fleets:

spec:
  updateStrategy:
    type: Auto
    schedule:
      windows:
        - start: "02:00"
          end: "06:00"
    maxConcurrentResizes: 10
    maxTotalCpuIncrease: "2000m"
    maxTotalMemoryIncrease: "4Gi"

See examples/12-scheduled-auto-mode.yaml for a complete example. If resizes are blocked unexpectedly, see the troubleshooting guide for schedule-specific diagnostics.

Exporting recommendations to ConfigMaps

The export feature writes recommendation data to ConfigMaps for external consumption (e.g., GitOps workflows with ArgoCD or Flux that apply resource patches from CI/CD rather than letting the operator resize directly).

spec:
  updateStrategy:
    type: Recommend  # or Auto
    export:
      configMap: true

When enabled, the operator creates one ConfigMap per workload, named <policy>-<workload>-recommendations, with an owner reference to the policy for automatic cleanup. The ConfigMap contains per-container recommended CPU and memory values.

This is useful in GitOps workflows where:

1. The operator runs in Recommend mode to compute recommendations.
2. A CI/CD pipeline reads the ConfigMaps and generates resource patches.
3. ArgoCD or Flux applies the patches through the normal GitOps flow.

Promoting from Recommend or Canary

From Recommend mode

kubectl patch rsp my-app --type merge \
  -p '{"spec":{"updateStrategy":{"type":"Auto","autoRevert":true}}}'

From Canary mode

kubectl patch rsp my-app --type merge \
  -p '{"spec":{"updateStrategy":{"type":"Auto"}}}'

Rollback

If Auto mode causes issues, switch back to Recommend immediately:

kubectl patch rsp my-app --type merge \
  -p '{"spec":{"updateStrategy":{"type":"Recommend"}}}'

This stops all future resizes. Already-resized pods keep their current resources until their next restart.