Edge Computing Patterns (2024+)

AI/ML at Edge

KubeFlow Edge Configuration

apiVersion: serving.kubeflow.org/v1beta1
kind: InferenceService
metadata:
  name: edge-inference
  annotations:
    serving.kubeflow.org/deploymentMode: EdgeInference
spec:
  predictor:
    minReplicas: 1
    maxReplicas: 3
    model:
      modelFormat:
        name: onnx
      storage:
        path: s3://models/edge-model
        key: model.onnx
    resources:
      limits:
        cpu: "2"
        memory: "4Gi"
        nvidia.com/gpu: "1"

Real-Time Processing

KEDA Event Processing

apiVersion: keda.sh/v1alpha1
kind: ScaledObject
metadata:
  name: edge-processor
spec:
  scaleTargetRef:
    name: stream-processor
  triggers:
  - type: kafka
    metadata:
      bootstrapServers: edge-kafka:9092
      consumerGroup: edge-group
      topic: sensor-data
      lagThreshold: "50"
---
apiVersion: apps/v1
kind: Deployment
metadata:
  name: stream-processor
spec:
  template:
    spec:
      containers:
      - name: processor
        image: edge-processor:latest
        resources:
          limits:
            memory: "1Gi"
            cpu: "500m"

Edge Storage

MinIO Edge Cache

apiVersion: minio.min.io/v2
kind: Tenant
metadata:
  name: edge-cache
spec:
  pools:
  - servers: 4
    volumesPerServer: 4
    size: 100Gi
    name: edge-pool
  - servers: 2
    volumesPerServer: 2
    size: 50Gi
    name: cache-pool
    allowedNetworks:
    - 192.168.0.0/16
  certificate:
    requestAutoCert: true

Best Practices

1. Edge Architecture
   • Local processing
   • Data filtering
   • Batch aggregation
   • Sync strategies
2. Performance
   • Low latency
   • Bandwidth usage
   • Cache optimization
   • Resource limits
3. Resilience
   • Offline operation
   • Data buffering
   • Error handling
   • State management
4. Security
   • Edge encryption
   • Access control
   • Data privacy
   • Network isolation