About This Tool

Primary Use Case

Key Features

• Supports Kubernetes HPA v2 with custom metrics via Prometheus
• Integrates with Kubernetes Metrics Server for core CPU and memory metrics
• Implements a custom API server registered with Kubernetes aggregation layer
• Enables autoscaling based on arbitrary application-specific metrics
• Compatible with Kubernetes 1.9 or later
• Includes demo application for testing autoscaling behavior
• Uses Prometheus as the metrics backend
• Provides step-by-step setup guide for Metrics Server and Prometheus integration

Insights & Recommendations

Ensure Kubernetes version 1.9 or later is used as the HPA rest client is enabled by default from this version onward. The Metrics Server must be deployed and operational before using this tool. Prometheus must be configured properly to expose custom metrics to the HPA controller. Using this tool requires familiarity with Kubernetes custom metrics API and Prometheus setup.

Installation

Install Go 1.8 or later
Set up GOPATH environment variable
Clone the repository: git clone https://github.com/stefanprodan/k8s-prom-hpa into $GOPATH
Deploy the Kubernetes Metrics Server in the kube-system namespace: kubectl create -f ./metrics-server
Wait approximately one minute for Metrics Server to start reporting metrics
Deploy the demo podinfo application to test autoscaling

Usage

Smart Usage Notes

• Integrate with CI/CD pipelines to automate scaling based on real-time security telemetry.
• Use custom metrics to detect anomalous application behavior indicative of attacks or misconfigurations.
• Combine with Kubernetes admission controllers for proactive security enforcement during autoscaling events.
• Leverage Prometheus metrics to trigger automated incident response workflows when thresholds are breached.
• Employ in purple team exercises to simulate dynamic threat scenarios with workload scaling impacts.

Security Capability Profile