How do cloud-native applications support elastic scaling based on demand?

Cloud-native applications achieve dynamic resource adjustment through cloud platform design. Their importance lies in ensuring high availability and efficient cost management. Application scenarios include handling peak traffic in e-commerce or demand fluctuations in microservice architectures.

They are corely based on containerization (such as Docker) and orchestration tools (such as Kubernetes). Features include auto-scaling mechanisms that trigger real-time resource changes by monitoring metrics like CPU and memory. In practical applications, it can automatically increase Pod instances during traffic surges, reduce waste, enhance system stability, and adapt to the scalability of cloud-native environments.

Implementation steps include configuring the Kubernetes Horizontal Pod Autoscaler, setting target metric thresholds (e.g., 80% CPU utilization), and minimum/maximum replica ranges. Typical scenarios include peak periods of promotional activities; business values are optimizing resource costs, enhancing response speed, and improving user experience satisfaction.