Kubernetes Auto-Scaling

What it is:

Kubernetes Auto-Scaling is the ability of a Kubernetes cluster to automatically adjust the number of running pods or nodes based on system load or defined metrics. It ensures that containerized workloads—such as identity services like PingFederate, PingDirectory, or Keycloak—can scale up during peak demand and scale down to save resources when traffic is low. Kubernetes supports multiple forms of auto-scaling, including the Horizontal Pod Autoscaler (HPA), Vertical Pod Autoscaler (VPA), and Cluster Autoscaler.

Why it matters:

For IAM platforms, availability and responsiveness are critical. Auto-scaling helps:

• Maintain performance under load (e.g., during authentication spikes)

• Ensure high availability of IAM components without over-provisioning

• Reduce costs by optimizing compute resource usage

• Enable zero-downtime user experiences, even during usage surges or node failuresThis is especially valuable in CIAM systems where authentication, authorization, or directory lookups must be reliable and fast at all times.
  

How it works:

Auto-scaling is typically configured using Kubernetes manifests or Infrastructure-as-Code tools like Helm and Terraform. IAM workloads are monitored using metrics (e.g., CPU, memory, custom Prometheus-based signals). Based on these metrics:

• HPA adds or removes pods based on workload pressure

• Cluster Autoscaler adjusts the number of worker nodes to meet demand

• Custom Metrics (e.g., login request rate, directory read latency) can be used to fine-tune IAM-specific behaviorMidships leverages Kubernetes auto-scaling in its deployment accelerators to deliver resilient, self-adjusting IAM stacks across cloud and hybrid environments.