Tag archive
Kubernetes custom metrics are not just an HPA trick. They are where platform teams admit CPU and memory are often the wrong pressure signals for real production systems.
Traditional auto-scaling fails when your users aren't just requesting data, but are deploying...
Technical comparison of ECS 20-second metrics against step scaling, scheduled scaling, and hybrid approaches in high-demand financial-grade environments.
A CKA Workloads & Scheduling walkthrough: autoscale a Deployment on CPU with a HorizontalPodAutoscaler. See why kubectl autoscale can't set a scaleDown stabilization window, author the autoscaling/v2 manifest (CPU target in metrics[] plus a behavior block), and verify min, max, target, and the 30s window, with real terminal output.
Introduction: Navigating the Kubernetes Optimization Labyrinth Kubernetes clusters,...
Modern applications rarely experience constant traffic. User demand fluctuates throughout the day,...
Originally published at norvik.tech Introduction Deep dive into Amazon ECS's new...
How does one URL serve millions of people when a single server would fall over? Two ideas: a load balancer that spreads traffic across many identical
Karpenter on AKS in 2026: What Actually Works Karpenter on AKS has gone from "interesting...
Idle GPUs at six dollars an hour are a bonfire. Scaling to zero saves the money, but the first user back waits minutes unless you kill the cold start.
Introduction Modern applications experience changing traffic patterns throughout the day....
Originally published on graycloudarch.com. The morning after go-live, the first thing I looked at...