Cluster Autoscaling for a Workload With Bursty Traffic Patterns

A consumer-facing service whose traffic patterns followed a predictable but bursty cadence — daily peaks tied to user behavior, weekly patterns tied to content release cycles, occasional spikes tied to external events. The infrastructure had been provisioned for the peak, which meant most capacity was idle most of the time.

The team was paying for capacity it wasn't using during off-peak hours, and was nervous about the times the burst exceeded the provisioned capacity. The architecture had no mechanism to expand or contract automatically; capacity changes were manual operational events that nobody wanted to do at the times they were actually needed.

A cluster autoscaling architecture matched to the workload's actual patterns.

Horizontal Pod Autoscaler tuned per service — Each service got an HPA configuration based on the load signal that actually correlated with its capacity needs — not always CPU, sometimes request rate or queue depth. The signals were chosen for relevance, not for default convenience.

Cluster Autoscaler with appropriate node groups — The cluster's node groups were structured so that the autoscaler could provision capacity quickly when needed and decommission it when load receded. Node group composition included Spot capacity for non-critical workloads, with appropriate disruption handling.

Predictive scaling for known patterns — For traffic patterns that were genuinely predictable — daily peaks at known times — pre-scaling was scheduled to ensure capacity was ready before the peak arrived rather than scaling reactively after the peak hit.

Disruption budgets and graceful shutdown — Pod disruption budgets ensured that scaling-down events didn't reduce service capacity below acceptable thresholds. Services were instrumented for graceful shutdown so that pods being terminated finished their in-flight work cleanly.

Cost dashboards — The team gained visibility into the relationship between traffic patterns and infrastructure cost. The cost-per-request curve became measurable, which informed further optimization decisions.

Infrastructure cost during off-peak hours dropped substantially. Capacity during peak and burst events was reliably available without manual intervention. The team's operational anxiety about traffic events decreased meaningfully because the architecture was now responsive to load rather than statically provisioned for the worst case.