Kubernetes makes it possible to autoscale various business use cases from web apps to mobile, IoT edge streaming, and AI/ML in more reliable and stable ways. One caveat of the Kubernetes autoscaling is based on hardware resource utilization (CPU, memory) through Horizontal Pod Autoscaling. This causes a new challenge to build an event-driven serverless Java on Kubernetes because the event metrics from multiple event sources (e.g., Apache Kafka, AWS SQS) are more relevant than a pod's CPU usage for deciding when applications need to be scaled out and in.
Fortunately, KEDA and Knative on Kubernetes are designed to solve this challenge by autoscaling both standard apps and serverless by event metrics in a separate way. This session will teach you how to redesign your Kubernetes autoscaling architecture by event-driven metrics from Apache Kafka over standard resources (CPU, Memory) with Knative and KEDA integration for serverless Java using Quarkus.