Infrastructure Scaling, Capacity Planning, and High Availability Questions

How to make running infrastructure scale and stay available: the operational and architectural mechanics of doing it, not the growth-modeling math behind it. Covers autoscaling policy design and Kubernetes cluster scaling (target-tracking, step, scheduled, and predictive triggers, cooldowns, warm pools, HPA, VPA, Cluster Autoscaler, GPU/node scheduling, bin-packing), load balancing algorithms and architecture (round-robin, least-connections, consistent hashing, L4 vs L7, health checks, connection draining, session affinity), horizontal versus vertical scaling choices, and high-availability and redundancy design (multi-AZ and multi-region failover, active-active vs active-passive, split-brain and leader election). Also covers turning a given demand or growth figure into concrete provisioning: headroom and safety-margin sizing, back-of-envelope instance, IOPS, and replica-count math, right-sizing, and multi-year procurement planning; validating a sizing or scaling change through load, stress, soak, and chaos testing; scaling stateful tiers such as databases, caches, and message queues; cost-aware trade-offs including reserved versus spot capacity and managed versus self-hosted infrastructure; and the observability needed to catch capacity saturation before it breaches an SLO.