Out-of-the-box, Kubernetes provides native horizontal scaling capabilities driven by conventional resource consumption signals like CPU and memory utilization. However, in the real world, numerous applications demand dynamic scaling orchestrated by custom business telemetry such as queue depths, throughput volumes, or other domain-specific indicators.
This session will unravel the secrets of extending Kubernetes' Horizontal Pod Autoscaler (HPA) to leverage custom metrics as scaling triggers, unlocking unprecedented scaling autonomy. Attendees will witness live demos showcasing:
Deploying a custom metrics provider to expose application-centric metrics to the Kubernetes control plane
Configuring the HPA to consume these custom metrics for intelligent scaling decisions
A sample application dynamically scaling based on a custom metric like queue length or requests per second
Best practices for crafting bespoke scaling policies tailored to custom metrics.