🏗️ Resilient IT Infrastructure
🏷️ Category: IT Infrastructure | 🔖 6 min read
👤 By James Lee | 📅 May 20, 2024
💡 "Downtime is expensive; resilience is profitable."
🔁 Redundancy
Modern enterprise infrastructure must be designed to eliminate single points of failure. This requires implementing multiple layers of redundancy across every critical component:
Geographic Redundancy: Deploy your infrastructure across multiple availability zones (AZs) and regions. AWS, Azure, and GCP all offer region-based deployments where your application can automatically failover between data centers that are hundreds of miles apart. This protects against natural disasters, regional power outages, and local network failures.
Data Redundancy: Implement synchronous replication for mission-critical data and asynchronous replication for less critical workloads. Use database clustering with master-slave configurations, and consider implementing distributed databases like Cassandra or MongoDB with built-in replication. Always maintain at least 3 copies of critical data using the 3-2-1 backup rule: 3 copies total, 2 local but on different media, 1 offsite.
Network Redundancy: Configure multiple network paths using Border Gateway Protocol (BGP) with different ISPs. Implement load balancers with health checks that can automatically route traffic away from failed instances. Use Content Delivery Networks (CDNs) like CloudFlare or AWS CloudFront to cache content globally and provide automatic failover.
↕️ Scalability
True scalability means your infrastructure can handle both predictable growth and unexpected traffic spikes without manual intervention:
resource "aws_autoscaling_group" "app_servers" {
min_size = 2
max_size = 20
desired_capacity = 4
launch_template {
id = aws_launch_template.app_server.id
version = "$Latest"
}
target_group_arns = [aws_lb_target_group.app.arn]
tag {
key = "Name"
value = "app-server-asg"
propagate_at_launch = true
}
}
resource "aws_autoscaling_policy" "scale_up" {
name = "scale-up"
scaling_adjustment = 2
adjustment_type = "ChangeInCapacity"
cooldown = 300
autoscaling_group_name = aws_autoscaling_group.app_servers.name
}
resource "aws_cloudwatch_metric_alarm" "high_cpu" {
alarm_name = "high-cpu-utilization"
comparison_operator = "GreaterThanThreshold"
evaluation_periods = "2"
metric_name = "CPUUtilization"
namespace = "AWS/EC2"
period = "60"
statistic = "Average"
threshold = "75"
alarm_description = "This metric monitors ec2 cpu utilization"
alarm_actions = [aws_autoscaling_policy.scale_up.arn]
}
Horizontal vs Vertical Scaling: Design your applications to scale horizontally (adding more instances) rather than vertically (upgrading hardware). This approach is more cost-effective and provides better fault tolerance. Use microservices architecture where each service can be scaled independently based on demand.
Database Scaling: Implement read replicas to distribute read queries across multiple database instances. Use database sharding for write-heavy applications, and consider NoSQL databases like DynamoDB or MongoDB for applications that need to scale beyond traditional relational database limits.
Serverless Architecture: Leverage AWS Lambda, Azure Functions, or Google Cloud Functions for event-driven workloads. Serverless computing automatically scales to zero when not in use and can handle thousands of concurrent requests without capacity planning.
🤖 Automation & IaC
Infrastructure as Code (IaC) is essential for maintaining consistent, reproducible, and scalable infrastructure:
Terraform Best Practices: Use modules to create reusable infrastructure components. Implement remote state management with state locking to prevent concurrent modifications. Use workspaces to manage multiple environments (dev, staging, production) with the same configuration.
CI/CD Pipeline Integration: Integrate your infrastructure changes with your application deployment pipeline. Use tools like GitLab CI, Jenkins, or GitHub Actions to automatically validate, plan, and apply infrastructure changes. Implement automated testing for your infrastructure code using tools like Terratest or Kitchen-Terraform.
Configuration Management: Use tools like Ansible, Puppet, or Chef to manage server configurations. Implement immutable infrastructure where servers are never modified after deployment – instead, create new instances with updated configurations and replace the old ones.
Secrets Management: Use dedicated secrets management services like AWS Secrets Manager, Azure Key Vault, or HashiCorp Vault. Never store sensitive information in plain text configuration files or environment variables.
🔍 Observability
Comprehensive observability is crucial for maintaining resilient infrastructure:
Monitoring Stack: Implement a complete monitoring solution using Prometheus for metrics collection, Grafana for visualization, and Alertmanager for alert routing. Use exporters to collect metrics from various services and infrastructure components.
Distributed Tracing: Implement distributed tracing using tools like Jaeger or Zipkin to track requests across microservices. This helps identify bottlenecks and understand the flow of requests through your system.
Log Management: Centralize logs using the ELK stack (Elasticsearch, Logstash, Kibana) or alternatives like Fluentd and Grafana Loki. Implement structured logging with consistent formats across all services to enable effective log analysis.
Alerting Strategy: Create meaningful alerts based on symptoms rather than causes. Use the Golden Signals: latency, traffic, errors, and saturation. Implement alert escalation policies and ensure alerts are actionable – every alert should have a clear response procedure.
Chaos Engineering: Regularly test your infrastructure's resilience using chaos engineering principles. Use tools like Chaos Monkey, Litmus, or Gremlin to intentionally introduce failures and verify that your systems can recover gracefully.
Published on May 20, 2024 • 6 min read