Autoscaling

Aevum Encyclopedia's intelligent autoscaling infrastructure dynamically adjusts computing resources to handle traffic from 100 to 10 million concurrent users — seamlessly, with sub-second response times.

🔭 Overview

Aevum Encyclopedia's autoscaling system is built on a multi-layered architecture that monitors traffic patterns, predictive load forecasting, and resource utilization across our global infrastructure. The system operates at three distinct levels: horizontal pod autoscaling, cluster autoscaling, and predictive scaling based on historical and real-time data.

🏛️ Architecture

Our autoscaling pipeline operates as a closed-loop control system with four critical stages: observation, analysis, decision, and action. Each stage operates independently with failover mechanisms ensuring zero single points of failure.

⚙️ Configuration

Autoscaling behavior is configured through our declarative YAML manifests. Each service tier (search, content delivery, AI inference, editorial tools) has independent scaling policies.

Horizontal Pod Autoscaler

apiVersion: autoscaling/v2
kind: HorizontalPodAutoscaler
metadata:
  name: aevum-content-api
  namespace: production
spec:
  scaleTargetRef:
    apiVersion: apps/v1
    kind: Deployment
    name: content-api
  minReplicas: 10
  maxReplicas: 2000
  metrics:
    - type: Resource
      resource:
        name: cpu
        target:
          type: Utilization
          averageUtilization: 65
    - type: Resource
      resource:
        name: memory
        target:
          type: Utilization
          averageUtilization: 80
    - type: Pods
      pods:
        metric:
          name: requests-per-second
        target:
          type: AverageValue
          averageValue: 500
  behavior:
    scaleUp:
      stabilizationWindowSeconds: 15
      policies:
        - type: Percent
          value: 100
          periodSeconds: 15
    scaleDown:
      stabilizationWindowSeconds: 300
      policies:
        - type: Percent
          value: 10
          periodSeconds: 60

Predictive Scaling Configuration

apiVersion: aevum.io/v1
kind: PredictiveScalePolicy
metadata:
  name: peak-traffic-policy
spec:
  forecastHorizon: 15m
  confidenceLevel: 0.95
  trainingData:
    historyWindow: 30d
    seasonality: true
    eventCorrelation: true
  triggers:
    - type: scheduled-event
      source: editorial-calendar
      leadTime: 30m
    - type: traffic-spike
      threshold: 3.0x  # 3x baseline
  override:
    enabled: true
    maxOverridePercent: 200

📋 Scaling Policies

Different service tiers have distinct scaling policies optimized for their workload characteristics. The table below outlines our default configurations.

⚙️ How It Works

The autoscaling pipeline operates continuously in a closed feedback loop. Here's the step-by-step process:

📊 Key Metrics

Monitor autoscaling performance through these core metrics, available via our observability dashboard and metrics API.

🔌 Scaling API

Manage autoscaling programmatically through our REST API. All endpoints require API key authentication.

Get Current Scale Status

GET /v3/infra/scale/status
Authorization: Bearer <api-key>
Host: api.aevum-encyclopedia.com

Response

{
  "status: "healthy,
  "regions: {
    "us-east-1: {
      "active_pods: 234,
      "cpu_utilization: 58.2,
      "memory_utilization: 71.4,
      "rps: 12450,
      "last_scale_event: "2025-01-15T14:32:11Z,
      "scale_direction: "none
    },
    "eu-west-1: {
      "active_pods: 189,
      "cpu_utilization: 62.8,
      "memory_utilization: 68.1,
      "rps: 9870,
      "last_scale_event: "2025-01-15T14:29:45Z,
      "scale_direction: "up
    }
  },
  "total_pods: 847,
  "global_rps: 67200,
  "forecast: {
    "next_5m: 72000,
    "next_15m: 85000,
    "confidence: 0.94
  }
}

Manual Override

POST /v3/infra/scale/override
Authorization: Bearer <api-key>
Content-Type: application/json

{
  "service: "content-api,
  "min_replicas: 50,
  "max_replicas: 500,
  "duration: "1h,
  "reason: "Scheduled maintenance window
}

✅ Best Practices

Additional recommendations:

• → Use pod disruption budgets (PDBs) to maintain availability during node maintenance and scaling events
• → Configure gradual scale-down windows (minimum 5 minutes) to prevent thrashing during traffic fluctuations
• → Leverage predictive scaling for known traffic events: editorial publications, featured content drops, and scheduled webinars
• → Monitor forecast accuracy weekly — declining accuracy indicates the need to retrain ML models with fresh data
• → Separate scaling policies for stateless APIs and stateful services; databases require different scaling strategies

🔧 Troubleshooting

💰 Cost Optimization

Autoscaling is inherently cost-efficient, but our platform provides additional optimization layers to minimize infrastructure spend while maintaining performance targets.

🔗 Related Resources