AWS Cloud Architecture
Designed and deployed 3+ production applications on AWS with 99.99% uptime guarantee. Implemented auto-scaling, multi-region failover, and cost optimization strategies using 15+ AWS services.
High-Availability Architecture
Uptime SLA
99.99% (Four Nines)
Failover Time
<5 seconds
Regions
2 (us-east-1, eu-west-1)
AWS Services
15+
Architecture Layers
1. DNS & Global Distribution (Route 53)
Route 53 geolocation routing directs users to nearest regional endpoint. Implements health checks with automatic failover to secondary region in <5 seconds.
Latency-based routing | Health checks (30s interval) | Weighted policies2. Content Delivery Network (CloudFront)
CloudFront edge locations cache static and dynamic content. Reduced latency by 60% vs direct origin access. Origin failover routes to secondary origin if primary is unavailable.
Origin Failover | Geo-restriction | Cache invalidation (1s TTL for dynamic)3. Load Balancing (Application Load Balancer)
ALB distributes traffic across EC2 instances in multiple availability zones. Sticky sessions for user continuity. Health checks every 10 seconds.
Path-based routing | Host-based routing | Connection draining4. Compute (Auto Scaling)
EC2 Auto Scaling Group scales instances 1-10 based on CPU >70%. Target tracking scaling policy maintains optimal cost/performance balance. <2 minute ramp-up time.
Min: 1 | Max: 10 | Target CPU: 70% | Cooldown: 300s5. Database Layer (RDS Multi-AZ)
RDS PostgreSQL with Multi-AZ deployment provides automatic failover in <2 minutes. Read replicas in secondary region for disaster recovery.
Multi-AZ | Automated backups (7-day retention) | Read replicas6. Object Storage & Backup (S3)
S3 versioning enables point-in-time recovery. Cross-region replication for disaster recovery. Lifecycle policies move old data to Glacier for cost optimization (60% savings on archived data).
Versioning | Cross-region replication | S3 → Glacier (30 days)7. Serverless Functions (Lambda)
Lambda handles asynchronous tasks (image processing, email notifications) triggered by S3 events or SQS queue. Eliminates need for dedicated servers for batch jobs.
Concurrent executions: 1000 | Runtime: Python 3.11 | Memory: 512 MB8. Monitoring & Alerting (CloudWatch)
CloudWatch dashboards track 50+ metrics (CPU, memory, network, application-level). SNS alerts notify ops team on threshold breaches (P1 within 5 min, P2 within 30 min).
Custom metrics | Log insights (real-time querying) | Anomaly detectionCost Optimization Strategies
💰 Reserved Instances (RI)
Purchased 3-year RIs for baseline capacity, reducing EC2 costs by 70% vs on-demand.
Savings: ₹3L/year
📊 Spot Instances
50% of compute capacity on Spot instances for non-critical workloads (batch processing, data analysis).
Savings: ₹50K/month
📦 S3 Lifecycle Policies
Old data auto-migrates to Cheaper Glacier storage after 30 days. Delete ineffective delete after 1 year.
Savings: ₹15K/month on storage
🔄 Lambda Over EC2 for Batch Jobs
Event-driven Lambda replaced cron-based EC2 jobs. Pay only for execution time (milliseconds), not idle time.
Savings: ₹20K/month
Total Monthly Savings
₹88,500/month (42% reduction)
Key Architectural Learnings
Redundancy Everywhere = Reliability
Multi-AZ RDS + Read replicas + S3 cross-region replication. Single point of failure = production nightmares.
Monitor First, Debug Later
CloudWatch custom metrics saved us hours. Anomaly detection caught a memory leak before users noticed.
Cost Optimization is Ongoing
42% savings came from iterative changes (RIs, Spot, Glacier). Review bills monthly; AWS offerings evolve constantly.