HPA Scaling Behavior

This document explains how autoscaling behavior is expected to work in the platform.

Inputs to Scaling

Horizontal Pod Autoscaling uses workload metrics such as:

• CPU utilization
• memory usage
• sustained application demand

Scaling Outcomes

When thresholds are exceeded:

• pod replica count increases

When demand falls:

• pod replica count decreases

Operational Considerations

Scaling should be validated through load testing to confirm:

• thresholds are appropriate
• scaling is responsive
• workloads remain stable during transitions

Autoscaling is not just a configuration feature; it is an operational behavior that must be understood and observed.