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Event-driven autoscaling for Serverless Java
Daniel Oh is a Senior Principal Developer Advocate at Red Hat to evangelize developers for building Cloud-Native Microservices and Serverless Functions with Cloud-Native Runtimes(i.e. Quarkus, Spring Boot, Node.js) and OpenShift/Kubernetes. Daniel also continues to contribute to various cloud open-source projects and ecosystems as a CNCF ambassador for accelerating DevOps adoption in enterprises. He’s speaking at lots of technical seminars, workshops, and meetups to elaborate on new emerging technologies for enterprise developers, SREs, Platform Engineers, and DevOps teams.
Abstract
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 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.
