How do you scale cloud-native application workloads with Kubernetes?

Kubernetes is an open-source container orchestration platform used to automate the management and scaling of workloads for cloud-native applications. Its importance lies in providing elastic scaling capabilities, ensuring applications efficiently respond to traffic fluctuations and resource demands, making it suitable for microservices architectures and dynamic cloud environments such as e-commerce or real-time data processing scenarios.

The core mechanism is the Horizontal Pod Autoscaler (HPA), which dynamically adjusts the number of Pod replicas based on CPU utilization, memory, or custom metrics. HPA monitors metrics and maintains target thresholds, automatically adding or removing Pods to balance loads, improving resource utilization, application availability and performance, while reducing operational costs and driving the elastic growth of cloud-native applications.

Implementation steps: First, deploy the application and expose monitoring metrics; second, configure HPA policies to specify targets and scaling ranges; finally, simulate load tests to verify behavior. Typical scenarios address sudden traffic surges, such as promotional events, with business values including optimizing infrastructure costs, enhancing system resilience, and accelerating business expansion.