Cloud Infrastructure at Scale - AWS, Kubernetes, and Beyond
by Thomas Carman
Designing Cloud-Native Infrastructure
Moving from traditional infrastructure to cloud-native architectures is a journey full of challenges and learning opportunities. I’ve been deep in the trenches of cloud infrastructure design, and here’s what I’ve learned about building scalable, resilient systems.
The Cloud-Native Mindset
Traditional infrastructure thinking doesn’t always translate well to the cloud. Key mental shifts:
Cattle vs. Pets
- Servers are disposable and ephemeral
- Automated recovery instead of manual fixes
- Infrastructure from code, not clicks
- Immutable infrastructure patterns
Embracing Failure
- Design for failure from day one
- Use multiple availability zones
- Implement circuit breakers
- Graceful degradation over complete failure
Infrastructure as Code (IaC)
Terraform: The Universal Language
Why Terraform became my IaC tool of choice:
- Multi-cloud support (AWS, Azure, GCP)
- Rich ecosystem of providers
- State management for tracking resources
- Plan before apply workflow
Example patterns:
# Modular, reusable infrastructure components
# Environment-specific variable files
# Remote state with S3 + DynamoDB locking
# Workspace separation for environments
Best Practices
- Version control everything
- Use modules for reusability
- Implement naming conventions
- Document all resources
- Regular state file backups
Kubernetes: Orchestration at Scale
Why Kubernetes?
- Container orchestration and scheduling
- Self-healing capabilities
- Horizontal scaling
- Service discovery and load balancing
- Rolling updates and rollbacks
Architecture Decisions
Managed vs. Self-Hosted
- Started with EKS (AWS managed Kubernetes)
- Reduced operational overhead
- Automatic control plane updates
- Better security patching
Cluster Organization
Production cluster: Critical workloads
Staging cluster: Pre-production testing
Development cluster: Developer experimentation
Namespace Strategy
- Per-service namespaces
- Environment separation
- Resource quotas and limits
- Network policies for isolation
Cloud Architecture Patterns
1. Microservices on Kubernetes
Service Mesh with Istio
- Traffic management and routing
- Service-to-service authentication
- Observability and tracing
- Circuit breaking and retry logic
Benefits realized:
- Independent scaling per service
- Language-agnostic architecture
- Isolated failure domains
- Faster deployment cycles
2. Serverless Components
When to use Lambda:
- Event-driven processing
- Irregular traffic patterns
- Short-lived operations
- Cost optimization for low-volume endpoints
Hybrid approach:
- Core services on Kubernetes
- Background jobs on Lambda
- API Gateway + Lambda for edge functions
- Best of both worlds
3. Data Layer Architecture
Database Strategy
- RDS PostgreSQL for transactional data
- DynamoDB for high-throughput key-value
- ElastiCache Redis for caching and sessions
- S3 for object storage
Backup & Recovery
- Automated daily snapshots
- Point-in-time recovery enabled
- Cross-region replication for critical data
- Tested disaster recovery procedures
Networking & Security
VPC Design
Public subnets: Load balancers, NAT gateways
Private subnets: Application servers
Database subnets: Isolated data layer
Multiple AZs for high availability
Security Layers
- Network: Security groups, NACLs, WAF
- Application: IAM roles, least privilege
- Data: Encryption at rest and in transit
- Monitoring: CloudWatch, GuardDuty, Security Hub
Key Security Practices
- No hardcoded credentials
- Secrets Manager for sensitive data
- Regular security audits
- Automated compliance checking
- Principle of least privilege everywhere
Observability Stack
Metrics, Logs, and Traces
Monitoring:
- Prometheus for metrics collection
- Grafana for visualization
- CloudWatch for AWS-native services
- Custom dashboards for business metrics
Logging:
- Fluentd for log aggregation
- ELK Stack for log analysis
- Structured JSON logging
- Centralized log storage
Tracing:
- Jaeger for distributed tracing
- Request ID propagation
- Performance bottleneck identification
- Error rate tracking per service
Cost Optimization
Strategies that saved 40% on cloud bills:
-
Right-Sizing Resources
- Regular review of instance utilization
- Automated scaling policies
- Spot instances for non-critical workloads
-
Storage Optimization
- S3 lifecycle policies
- Intelligent-Tiering for variable access patterns
- EBS volume optimization
-
Reserved Capacity
- Reserved instances for predictable workloads
- Savings plans for flexibility
- Commitment for 1-3 years
-
Monitoring & Alerts
- Cost anomaly detection
- Budget alerts
- Per-service cost allocation tags
Disaster Recovery
Multi-Region Strategy
Active-Passive Setup:
- Primary region: All traffic
- Secondary region: Hot standby
- Regular failover testing
- Automated DNS switching
Recovery Objectives:
- RTO (Recovery Time Objective): < 15 minutes
- RPO (Recovery Point Objective): < 5 minutes
- Automated failover procedures
- Documented runbooks
Automation & Self-Service
Developer Platform
- One-command environment creation
- Self-service database provisioning
- Automated SSL certificate management
- Pre-configured monitoring and alerting
GitOps Workflows
- Infrastructure changes via pull requests
- Automatic syncing with ArgoCD
- Audit trail for all changes
- Easy rollbacks
Performance Engineering
Load Testing
- Regular load tests with k6
- Identify bottlenecks before production
- Capacity planning based on data
- Automated performance regression tests
CDN Strategy
- CloudFront for static assets
- Edge caching for API responses
- Geographic distribution
- Reduced latency for global users
Database Optimization
- Read replicas for scaling reads
- Connection pooling
- Query optimization and indexing
- Caching frequently accessed data
Migration Strategies
Moving Legacy Systems to Cloud
Approach:
- Assess: Identify dependencies and requirements
- Containerize: Package applications in Docker
- Pilot: Start with non-critical services
- Iterate: Gradually move more services
- Optimize: Refactor for cloud-native patterns
Lessons Learned:
- Start with stateless services
- Plan for data migration carefully
- Maintain rollback capabilities
- Over-communicate with stakeholders
Real-World Challenges
Challenge 1: Database Migration
- Zero-downtime migration using read replicas
- Dual-write period for data consistency
- Extensive testing and validation
- Successful cutover with < 1 second downtime
Challenge 2: Cost Explosion
- Unmonitored resources accumulated
- Implemented tagging strategy
- Automated cleanup of unused resources
- 50% cost reduction in 3 months
Challenge 3: Scaling Bottlenecks
- Identified with APM tools
- Database connection pool exhaustion
- Added connection pooling layer
- Implemented caching strategy
Future Directions
Exploring:
- Service Mesh adoption (Istio/Linkerd)
- eBPF for advanced networking and observability
- ARM-based instances for cost savings
- Edge computing for lower latency
Key Takeaways
- Start with managed services: Focus on business value, not operations
- Automate everything: Manual processes don’t scale
- Design for failure: It’s not if, but when
- Monitor relentlessly: You can’t fix what you can’t see
- Cost is a feature: Build cost awareness into architecture
- Security by default: Easier to start secure than retrofit
- Document decisions: Your future self will thank you
Metrics of Success
After 6 months of cloud-native transformation:
- 99.95% uptime across all services
- 40% cost reduction through optimization
- 10x faster deployment velocity
- 80% reduction in manual operations
- Zero security incidents
Closing Thoughts
Cloud infrastructure is not just about lifting and shifting—it’s about fundamentally rethinking how we build and operate systems. The journey requires continuous learning, experimentation, and adaptation.
The best cloud architecture is one that enables your team to move fast while staying secure and cost-effective. It’s a balance of trade-offs, and the right answer always depends on your specific context.
Working on cloud infrastructure challenges? Let’s connect and share experiences!