Elastic scaling in cloud-native deployments is achieved through automated resource management based on real-time demand, leveraging container orchestration platforms like Kubernetes. Here's how it works:
Horizontal Pod Autoscaler (HPA): Kubernetes automatically adjusts the number of pod replicas based on metrics like CPU utilization, memory usage, or custom metrics (e.g., request latency). For example, if a web application experiences a traffic spike, HPA scales out by adding more pods to handle the load and scales in when traffic decreases.
Cluster Autoscaler: This component dynamically adjusts the size of the underlying node pool (VMs) to ensure sufficient resources for running pods. If pods cannot be scheduled due to resource constraints, the cluster autoscaler adds nodes; if nodes are underutilized, it removes them.
Serverless Functions: Cloud-native platforms often support serverless computing (e.g., Tencent Cloud's SCF - Serverless Cloud Function), where functions scale automatically per invocation without manual intervention. For instance, a backend API processing image uploads can scale instantly during peak usage.
Example: An e-commerce platform using Kubernetes deploys microservices for product listings, payments, and orders. During a flash sale, HPA detects increased CPU load on the payment service and spins up additional pods. Simultaneously, the cluster autoscaler provisions more nodes to accommodate the new pods, ensuring seamless performance.
Tencent Cloud Recommendation: Use Tencent Kubernetes Engine (TKE) with HPA and Cluster Autoscaler, combined with SCF for event-driven workloads, to achieve efficient elastic scaling.