Atlas › 05 Performance & Chaos Engineering › Kubernetes Scaling and Container Performance Testing☰ Read as one page
Kubernetes Scaling and Container Performance Testing
14.1Validating Kubernetes Auto-ScalingKubernetes Horizontal Pod Autoscaler (HPA) promises automatic scaling based on CPU, memory, or custom metrics. But "configured" does not…14.2k6 Test for HPA ValidationWhile k6 runs, monitor the Kubernetes cluster in a parallel terminal or dashboard:14.3HPA Configuration for PerformanceCPU-based HPA is often too slow for traffic-driven scaling. Custom metrics based on request rate can be more responsive:14.4Performance Testing Architecture Decision MatrixDifferent architectures have different performance concerns. Use this matrix to select the right testing strategy:14.5Testing Cascading Failures in MicroservicesIn a microservices architecture, a slow downstream service can cause cascading failures upstream. Test this scenario explicitly:14.6Resource Limits and PerformanceKubernetes resource requests and limits directly affect performance. Under-provisioned containers throttle at the worst possible moment:14.7CI Pipeline Integration: Full Performance and Chaos WorkflowThis pipeline ensures that every merge to main is validated for performance (Lighthouse + k6) and resilience (Litmus chaos). The system…