How do you scale cloud-native applications dynamically based on real-time usage metrics?

Dynamic scaling automatically adjusts the resource capacity of cloud-native applications based on real-time usage metrics, enhancing scalability, cost-effectiveness, and availability. It is crucial for handling demand fluctuations such as e-commerce peak periods or sudden traffic surges, ensuring stable response times and performance.

The core components include monitoring systems (e.g., Prometheus) that collect metrics (such as CPU usage or request rates), and scaling controllers (e.g., Kubernetes HPA) that set thresholds to trigger the increase or decrease of replicas. The principle is based on algorithms that automatically balance loads, optimize resource utilization, reduce waste, support highly available architectures, and improve overall operational efficiency.

Implementation steps: First, deploy metric monitoring tools; then configure HPA policies, defining target metric thresholds, minimum/maximum number of Pods, and cooldown periods; finally, test the policy response to real loads to achieve elastic scaling, thereby reducing costs and maintaining service quality.