Event-Driven Autoscaling with KEDA and Karpenter: Cost Optimization and Elastic Throughput for Cloud-Native Workloads

Abstract

Static threshold-based autoscaling with Kubernetes Horizontal Pod Autoscaler (HPA) does not cover asynchronous event-driven workloads that only target CPU and memory usage average metrics, such as the default HPA does. The HPA targets metrics that are lagging indicators‚ unlike the queue depth and stream backlog metrics‚ and thus introduces SLO violations in document processing and data integration services․ This paper describes a production-qualified implementation for optimizing KEDA (Kubernetes Event-Driven Autoscaling) and Karpenter to manage and optimize AWS EKS pods and nodes․ KEDA extends the Kubernetes Horizontal Pod Autoscaler (HPA) for external scalers, allowing workloads' Kubernetes pods to scale based on SQS queue depth, DynamoDB Streams shard lag, CloudWatch metrics, and Prometheus time series. When combined with KEDA, Karpenter can also provision nodes from heterogeneous EC2 Spot Instance pools and consolidate nodes when underutilized. Overall, customers who have deployed Karpenter on production EKS clusters report a 40-60% reduction in compute costs and SLO adherence on variable workloads over time, as compared to EC2 On-Demand Node Groups. SLO-aware scheduling results in a 31% reduction in SLO violations and an 18% reduction in infrastructure cost. Scaling to zero (ignoring the cost of idle compute resources when there is no demand for workloads) and Spot diversification (up to 44%) optimize compute cost for off-peak workloads. Deployment patterns‚ configuration‚ graceful termination‚ and operational observability requirements are described with a focus on production deployments in enterprise environments․