Capacity Planning and Resource Scaling

Design reactive and predictive auto-scaling policies for cloud workloads, covering HPA, VPA, KEDA, AWS ASGs, and target-tracking strategies.

Forecast cloud resource demand using historical trends, seasonality models, and workload growth projections to prevent over-provisioning and outages.

Identify over-provisioned and under-utilized cloud instances, recommend optimal instance types, and reduce waste without sacrificing performance or reliability.

Plan and scale database capacity for relational and NoSQL systems — covering read replicas, sharding, connection pooling, storage growth, and managed DB tier selection.

Translate cloud infrastructure capacity plans into financial forecasts, budget models, and cost projections aligned with engineering roadmaps and business growth targets.

Design and tune Kubernetes ResourceQuotas, LimitRanges, and requests/limits to ensure fair scheduling, cost control, and cluster stability at scale.

Plan cloud resource distribution across multiple regions and availability zones to balance latency, cost, redundancy, and capacity for global workloads.

Plan infrastructure capacity for high-traffic events, product launches, and seasonal spikes — ensuring uptime with load simulation, pre-scaling, and fallback strategies.

Design fault-tolerant spot and preemptible instance capacity strategies for AWS, GCP, and Azure to maximize cost savings while managing interruption risk.

Model object, block, and file storage growth trajectories, plan tiering strategies, and forecast storage costs to prevent capacity crises and optimize data lifecycle costs.