Passive Health Checks

Passive health checks monitor actual client traffic to detect failures.

Implementation Example: Envoy Outlier Detection

clusters:
- name: backend_service
  connect_timeout: 0.25s
  type: STRICT_DNS
  lb_policy: ROUND_ROBIN
  load_assignment:
    cluster_name: backend_service
    endpoints:
    - lb_endpoints:
      - endpoint:
          address:
            socket_address:
              address: backend1.example.com
              port_value: 80
  outlier_detection:
    consecutive_5xx: 5
    interval: 10s
    base_ejection_time: 30s
    max_ejection_percent: 50

Circuit Breaking

Circuit breaking prevents cascading failures by temporarily removing failing servers from the pool.

Implementation Example: Istio Circuit Breaking

apiVersion: networking.istio.io/v1alpha3
kind: DestinationRule
metadata:
  name: reviews
spec:
  host: reviews
  trafficPolicy:
    connectionPool:
      tcp:
        maxConnections: 100
      http:
        http1MaxPendingRequests: 1024
        maxRequestsPerConnection: 10
    outlierDetection:
      consecutiveErrors: 5
      interval: 5s
      baseEjectionTime: 30s
      maxEjectionPercent: 50

Load Balancing in Different Environments

Load balancing strategies vary based on the deployment environment and infrastructure.

Cloud-Native Load Balancing

Cloud providers offer managed load balancing services with advanced features.

Implementation Example: AWS Application Load Balancer

resource "aws_lb" "application_lb" {
  name               = "application-lb"
  internal           = false
  load_balancer_type = "application"
  security_groups    = [aws_security_group.lb_sg.id]
  subnets            = aws_subnet.public.*.id
  
  enable_deletion_protection = true
  
  access_logs {
    bucket  = aws_s3_bucket.lb_logs.bucket
    prefix  = "application-lb"
    enabled = true
  }
}

resource "aws_lb_target_group" "app_tg" {
  name     = "app-target-group"
  port     = 80
  protocol = "HTTP"
  vpc_id   = aws_vpc.main.id
  
  health_check {
    enabled             = true
    interval            = 30
    path                = "/health"
    port                = "traffic-port"
    healthy_threshold   = 3
    unhealthy_threshold = 3
    timeout             = 5
    protocol            = "HTTP"
    matcher             = "200"
  }
}

resource "aws_lb_listener" "front_end" {
  load_balancer_arn = aws_lb.application_lb.arn
  port              = "443"
  protocol          = "HTTPS"
  ssl_policy        = "ELBSecurityPolicy-2016-08"
  certificate_arn   = aws_acm_certificate.cert.arn
  
  default_action {
    type             = "forward"
    target_group_arn = aws_lb_target_group.app_tg.arn
  }
}

Kubernetes Load Balancing

Kubernetes provides built-in load balancing through Services and Ingress resources.

Implementation Example: Kubernetes Service and Ingress

# Service for internal load balancing
apiVersion: v1
kind: Service
metadata:
  name: backend-service
spec:
  selector:
    app: backend
  ports:
  - port: 80
    targetPort: 8080
  type: ClusterIP
---
# Ingress for external load balancing
apiVersion: networking.k8s.io/v1
kind: Ingress
metadata:
  name: backend-ingress
  annotations:
    nginx.ingress.kubernetes.io/rewrite-target: /
    nginx.ingress.kubernetes.io/ssl-redirect: "true"
    nginx.ingress.kubernetes.io/affinity: "cookie"
    nginx.ingress.kubernetes.io/session-cookie-name: "route"
    nginx.ingress.kubernetes.io/session-cookie-expires: "172800"
    nginx.ingress.kubernetes.io/session-cookie-max-age: "172800"
spec:
  rules:
  - host: api.example.com
    http:
      paths:
      - path: /
        pathType: Prefix
        backend:
          service:
            name: backend-service
            port:
              number: 80
  tls:
  - hosts:
    - api.example.com
    secretName: api-tls-cert

On-Premises Load Balancing

On-premises environments often use hardware or software load balancers.

Implementation Example: F5 BIG-IP Configuration

ltm virtual api_virtual {
    destination 192.168.1.100:443
    ip-protocol tcp
    mask 255.255.255.255
    pool api_pool
    profiles {
        http { }
        tcp { }
        clientssl {
            context clientside
        }
    }
    source 0.0.0.0/0
    translate-address enabled
    translate-port enabled
}

ltm pool api_pool {
    members {
        server1:80 {
            address 10.0.0.10
        }
        server2:80 {
            address 10.0.0.11
        }
        server3:80 {
            address 10.0.0.12
        }
    }
    monitor http
    load-balancing-mode least-connections-member
}

ltm monitor http api_health {
    defaults-from http
    destination *:*
    interval 5
    time-until-up 0
    timeout 16
    send "GET /health HTTP/1.1\r\nHost: api.example.com\r\nConnection: close\r\n\r\n"
    recv "HTTP/1.1 200 OK"
}

Best Practices for Load Balancing

To maximize the effectiveness of your load balancing strategy, consider these best practices:

1. Design for Failure

  • Assume components will fail and design accordingly
  • Implement proper health checks and failure detection
  • Use circuit breakers to prevent cascading failures
  • Test failure scenarios regularly

2. Monitor and Adjust

  • Collect metrics on server health and performance
  • Monitor load distribution across servers
  • Adjust load balancing parameters based on observed behavior
  • Set up alerts for imbalanced load distribution

3. Consider Session Persistence

  • Implement session persistence when required by the application
  • Use cookies or other client identifiers for sticky sessions
  • Balance persistence with even load distribution
  • Have a fallback strategy if the preferred server is unavailable

4. Optimize for Your Workload

  • Choose algorithms based on your specific workload characteristics
  • Consider request complexity and processing time variations
  • Adjust for heterogeneous server capabilities
  • Test with realistic traffic patterns

5. Layer Your Approach

  • Combine global, regional, and local load balancing
  • Use different strategies at different layers
  • Implement both client-side and server-side load balancing where appropriate
  • Consider specialized load balancing for different types of traffic

Conclusion

Effective load balancing is essential for building reliable, scalable distributed systems. By understanding the various algorithms, patterns, and implementation approaches, you can select the right strategy for your specific requirements.

Remember that load balancing is not a one-time setup but an ongoing process that requires monitoring, tuning, and adaptation as your system evolves. By following the best practices outlined in this article and selecting the appropriate load balancing strategy for your environment, you can ensure optimal performance, reliability, and resource utilization in your distributed systems.

Whether you’re running in the cloud, on Kubernetes, or in an on-premises data center, the principles of effective load balancing remain the same: distribute load evenly, detect and respond to failures quickly, and optimize for your specific workload characteristics.