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.