Requirements & goals: support up to 10x seasonal peak with SLOs (p99 latency < X ms, availability 99.95%). Budget constraint target and acceptable degradation window during ramp.
Metrics to track (real-time + historical):
- Traffic: requests/sec, input bytes/sec, distinct keys
- Utilization: CPU, memory, disk I/O, network
- Service-level: p50/p95/p99 latency, error rate, timeouts
- Queueing: queue lengths, backlog growth rate, processing rate
- Platform: instance boot time, container startup time, cold-cache miss rate
- Business: revenue-impacting requests, SLA breaches
Autoscaling strategies:
- Reactive horizontal autoscaling (target CPU, request latency, or custom metric like in-flight requests per host) with conservative cooldowns to prevent thrash.
- Predictive scaling using historical seasonality (time-series forecasting: ARIMA/Prophet/ML) to start scale-out before expected spike.
- Hybrid: predictive scale-up + reactive fine-tuning. Use rate-based and queue-length based policies to capture bursts.
- Use graceful draining and connection-aware load balancers; ensure state is externalized.
Pre-warming / reserved capacity:
- Maintain a warm pool of standby instances or containers (fraction of expected peak) for fast handoff.
- Use reserved/committed instances for base baseline and autoscaling for incremental capacity. Consider savings plans for steady baseline.
- For spot/interruptible instances, mix with reserved for cost: keep critical portion on reserved, opportunistic capacity for bulk.
Testing for scale:
- Rehearsals: regular load tests that simulate 1x–10x traffic including realistic request patterns and cache-warm vs cold scenarios.
- Chaos and failure injection (instance termination, AZ outage) during scaled conditions.
- Canary and gradual rollout during real spikes.
- Measure boot times, cache warm-up curves, and end-to-end SLOs.
Trade-offs (cost vs availability):
- 100% reserved capacity avoids cold starts but is costly. Heavily predictive scaling reduces cold time but risks overprovisioning if forecasts miss.
- Spot instances reduce cost but increase risk; use for noncritical batch or as pooled capacity with fast replacement.
- SLA targets dictate min reserved capacity: higher availability -> higher reserved %.
- Optimize by caching, batching, request shaping, and graceful degradation for low-value requests.
Operational pieces:
- Clear runbooks for scale events, automated alerts on forecast deviations.
- Dashboards for metrics and cost.
- Post-mortems after spikes to refine forecasts and policies.
Example: baseline 1000 rps, peak 10k rps. Reserve capacity for 2x baseline (handles growth + safety), predictive autoscale to 8x starting 2 hours before expected peak, warm pool to cover remaining 0–20% sudden bursts, reactive autoscale with queue-length target to reach full 10x. Adjust based on rehearsals and cost SLO.
