Explains how Kubernetes auto-scaling enables apps and clusters to adapt to changing workloads via metrics (CPU, memory, custom) that drive Horizontal Pod Autoscaling and Cluster Autoscaling, reducing waste, latency, and costs; details the collect-calculate-act workflow, best practices (clear policies, monitoring, strategy, rolling updates), and real-world uses in e-commerce, IoT, and real-time analytics.
Building scalable and efficient systems requires capacity planning and auto-scaling strategies to handle increased demand without crashing or wasting resources, avoiding downtime, inefficient resource allocation, and poor user experience.
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