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Video of Kubernetes Scaling Strategies Explained in Kubernetes course by KodeKloud channel, video No. 63 free certified online
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.
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