📈 Azure Scaling & Load Balancing - Advanced

Predictive Scaling

from azure.monitor.query import MetricsQueryClient
import numpy as np

def predict_required_capacity(historical_data):
  # Use linear regression to forecast
  X = np.arange(len(historical_data)).reshape(-1, 1)
  y = np.array(historical_data)
  
  from sklearn.linear_model import LinearRegression
  model = LinearRegression()
  model.fit(X, y)
  
  future_X = np.arange(len(historical_data), len(historical_data) + 24).reshape(-1, 1)
  return model.predict(future_X)

Application Gateway Advanced Routing

# URL-based routing
az network application-gateway url-path-map rule create \
  --gateway-name myAppGateway \
  --name "/api/*" \
  --paths "/api/*" \
  --address-pool apiBackendPool \
  --http-settings apiHttpSettings

Front Door for Global Load Balancing

# Create Front Door
az network front-door create \
  --name myFrontDoor \
  --resource-group myResourceGroup \
  --routing-rule-name myRoute \
  --frontend-host-name myapp.azurefd.net \
  --backend-address mybackend.azurewebsites.net

Key Takeaways

• **Predictive scaling** anticipates demand

• **Global routing** reduces latency

• **Advanced rules** enable sophisticated routing

• **Monitoring** drives decisions

Next Steps

Explore container orchestration with AKS.