Autoscaling Control-Loop Contract Design

Wrote docs/architecture/tasks/banking-autoscale-001/autoscaling-design/autoscaling-contract.md. Complete autoscaling control-loop contract establishing KEDA arrival-rate metrics, rapid scale-up policies, 300s scale-down stabilization windows, and anti-flapping hysteresis guards.

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skill: autoscaling-design

Autoscaling Control-Loop Contract: Merchant Payment Ingress [AUTOSCALE-PAY-001]

Summary

This specification establishes the application autoscaling control-loop contract, KEDA custom metric pipelines, stabilization windows, and flapping mitigation rules for merchant-payment-ingress v3.2 under run ID banking-autoscale-001. It governs Kubernetes Horizontal Pod Autoscaler (HPA) policies across AWS EKS sustaining traffic fluctuations from 2,500 requests/second to flash surges of 24,000 requests/second within 90 seconds. It decisively resolves the scaling lag and cluster flapping demonstrated in incident INC-3894 (where relying on raw CPU utilization delayed scale-out by 4 minutes, dropping 35% of incoming payment authorizations, followed by rapid scale-down thrashing that crashed cluster CoreDNS). The contract enforces

open-loop request arrival rate scaling via KEDA, sets a target throughput of

150 requests/sec per pod, configures rapid step-scaling (+100% pods per 15 seconds), mandates a

300-second scale-down stabilization window, and restricts replica bounds between 20 (floor) and 180 (ceiling).

Detailed Description

Relying on reactive resource metrics (such as CPU or Memory utilization) for application autoscaling introduces fatal lag during flash crowd arrivals. Pod CPU utilization is a lagging indicator: by the time worker CPU usage crosses an 70% threshold, incoming TCP queues and reverse proxy buffers have already saturated, resulting in dropped connections. Custom application metrics (such as Prometheus request arrival rates or queue depths) provide immediate leading signals, triggering rapid horizontal scaling before resource starvation occurs.

Incoming Flash Surge (2,500 -> 24,000 req/sec in 90s)
                         │
                         ▼
[ Ingress Gateway: Prometheus Scrape Point ]
  ├── Emits Leading Metric: `http_requests_per_second`
  └── Scraped by Prometheus Operator every 5 seconds
                         │
                         ▼
[ KEDA Controller: ScaledObject Evaluator ]
  ├── Compares Ingress Rate to Target: 150 req/sec/pod
  └── Computes Desired Replicas: 24,000 / 150 = 160 Pods
                         │
        ┌────────────────┴────────────────┐
        ▼ (Scale-Up: Immediate Action)    ▼ (Scale-Down: Stabilization Delay)
[ Fast Step Scaling (+100% / 15s) ] [ 300s Stabilization Window (Cooldown) ]
  ├── Pods scale 20 ──► 40 ──► 80 ──► 160 ├── Evaluates max recommendation over 5m
  └── Pre-warmed readiness in < 18s   └── Prevents flapping and DNS thrashing

Criteria and weights

Comparison

Result

Option C is selected. KEDA request-rate triggers scale pods preemptively; HPA behavior blocks enforce a 300-second scale-down stabilization window to eliminate thrashing.

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Required Mechanisms

1. Workload Scaling Envelope & Replica Bounds [MC-SE-01]

• Minimum Replica Floor: 20 pods (guarantees baseline absorption of 3,000 req/sec without latency spikes).

Maximum Replica Ceiling:

180 pods (caps compute consumption to prevent downstream database connection exhaustion).

• Workload Density: Target concurrency of 150 requests/second per pod under nominal p99 <= 25 ms.

2. Scaling Metric Pipeline (KEDA Prometheus Scaler) [MC-MP-01]

• KEDA ScaledObject Specification:

  apiVersion: keda.sh/v1alpha1
  kind: ScaledObject
  metadata:
    name: payment-ingress-scaler
    namespace: payments-prod
  spec:
    scaleTargetRef:
      name: merchant-payment-ingress
    minReplicaCount: 20
    maxReplicaCount: 180
    pollingInterval: 5
    cooldownPeriod: 300
    triggers:
    - type: prometheus
      metadata:
        serverAddress: http://prometheus-k8s.monitoring.svc:9090
        metricName: http_requests_per_second
        threshold: '150'
        query: sum(rate(http_requests_total{app="merchant-payment-ingress"}[1m]))
  

3. Stabilization Windows & HPA Step Policies [MC-SP-01]

• Scale-Up Behavior (Aggressive & Unbounded):

  scaleUp:
    stabilizationWindowSeconds: 0
    policies:
    - type: Percent
      value: 100
      periodSeconds: 15
    - type: Pods
      value: 30
      periodSeconds: 15
    selectPolicy: Max
  

  Result: Allows doubling replica count every 15 seconds during surges.
• Scale-Down Behavior (Conservative & Stabilized):

  scaleDown:
    stabilizationWindowSeconds: 300
    policies:
    - type: Percent
      value: 10
      periodSeconds: 60
  

  Result: Smoothly sheds at most 10% of pods per minute after 5 minutes of quiet traffic.

4. Anti-Flapping Hysteresis Guards [MC-AF-01]

Hysteresis Band: Scaler ignores rate variations within +- 10% of the target threshold (135 to 165 req/sec/pod) to prevent constant pod churning under micro-bursts.

Pod Readiness Gate: Pods configure readiness probes with initial delay of 5 seconds, ensuring new pods accept traffic in < 18 seconds from creation.

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Invariants and Contracts

Leading Metric Autoscaling Invariant [INV-AUTOSCALE-01]
  Production ingress services subject to flash surges must scale on leading application throughput metrics.
  Relying exclusively on lagging CPU or Memory utilization for horizontal pod autoscaling is prohibited.

Mandatory Scale-Down Stabilization Window [INV-AUTOSCALE-02]
  Horizontal Pod Autoscalers must enforce a scale-down stabilization window of at least 300 seconds (5 minutes).
  Instantaneous scale-down policies that induce pod flapping are strictly barred.

Strict Maximum Replica Bounds Ceiling [INV-AUTOSCALE-03]
  Every autoscaling workload must declare an immutable maximum replica ceiling (180).
  Unbounded autoscalers that risk cloud quota exhaustion or downstream database failure are prohibited.

Explicit Unknowns

• AWS EKS VPC CNI IP address allocation latency when spinning up 100 pods simultaneously across 3 subnets (G-1).
• Envoy ingress upstream connection draining behavior during 10% scale-down termination steps (G-2).

Traceability

Verification

No validator was supplied, so no command was run.

Reviewer self-check against autoscaling control-loop standards:

• Leading Indicator Safety: PASS. KEDA request arrival rate scaling replaces lagging CPU metrics.
• Reaction Speed: PASS. +100% per 15s step policy scales from 20 to 160 pods in < 60 seconds.
• Flapping Mitigation: PASS. 300s stabilization window and 10% step-down rate eliminate thrashing.
• Markdown Hygiene: PASS. Native Markdown syntax strictly adheres to rule_markdown.md.

Open Decisions

• DEC-AUTOSCALE-01: David O'Reilly to determine whether Karpenter node autoscaling should be coupled to KEDA metric events to pre-provision EC2 compute nodes ahead of pod scheduling (Owner: David O'Reilly).

Next steps

1. Marcus Vance installs KEDA operator v2.13 on production AWS EKS clusters.
2. Platform team applies the ScaledObject manifest to merchant-payment-ingress.
3. Conduct staging flash-surge drill injecting 24,000 req/sec to verify pod scale-out completes in under 90 seconds.