Learn Kubernetes with KodeKloud - Kubernetes Scaling Strategies Explained 👉

Kubernetes Scaling Strategies Explained 👉 • Horizontal Pod Autoscaling (HPA) Automatically increases or decreases the number of pods based on CPU, memory, or custom metrics. • Vertical Pod Autoscaling (VPA) Adjusts CPU and memory resources of existing pods to match workload needs (may require pod restarts). • Cluster Autoscaling Adds or removes nodes in the cluster when pods cannot be scheduled due to resource shortages. • Manual Scaling Scaling performed manually using kubectl scale - simple but not ideal for dynamic workloads. • Predictive Scaling Uses historical data and ML-based forecasts (e.g., KEDA) to scale workloads before demand spikes. • Custom Metrics-Based Scaling Scales workloads using application-level metrics (queue length, request rate, latency, etc.) via HPA. #kubernetes #kubernetesscaling #autoscaling #horizontalpodautoscaling #verticalpodautoscaling #kodekloud