Designing for Dynamic Scaling, High Availability, Fault Tolerance, and Reliability
Start from the requirement, not the service
HA within a Region usually means eliminating single points of failure across Availability Zones (Multi-AZ). Fault tolerance or Region-loss survival pushes you to multi-Region, which forces an explicit RTO/RPO decision and therefore a DR pattern: backup and restore (hours), pilot light, warm standby, or active-active (near zero).
Concepts you must be able to name
Stateless tiers with session state externalized to ElastiCache or DynamoDB; loose coupling via SQS, SNS, EventBridge, and Step Functions for long-running orchestration; idempotent consumers with retries using exponential backoff and jitter; Auto Scaling with target tracking as the default, predictive scaling for cyclic load, and scheduled actions for known events; Route 53 failover, weighted, and latency routing backed by health checks; and static stability, meaning failover should not depend on a control-plane action succeeding at the moment of an outage.
Traps
RDS Multi-AZ is a failover mechanism, not a read-scaling one, and a read replica is not a transparent failover target. S3 durability of eleven nines is not the same as availability. A single NAT gateway, a single-AZ Aurora cluster, or an Auto Scaling group pinned to one subnet breaks the AZ story while still looking Multi-AZ on a whiteboard. Instance store is ephemeral. Scaling on CPU when the real bottleneck is queue depth or connection count is the classic wrong answer.
Worked example
A 99.99% three-tier application: ALB across three AZs, one Auto Scaling group spanning three subnets, Aurora with a reader in each AZ, DynamoDB for session state, SQS between the web and worker tiers, idempotency keys in the worker, and a Route 53 failover record to a warm-standby Region with a documented 15-minute RTO.