This demo presents a self-operating Kubernetes (K8s) cluster that uses
digital twinning and machine learning to autonomously adapt its Horizontal
Pod Autoscaler (HPA) to workload changes. The demo uses a digital twin of a K8s cluster to gather performance statistics and learn a model for the
workload. With the model, the cluster autonomously adjusts HPA parameters for better performance. The demo illustrates this process and shows that the requested pod seconds decrease by ∼37 \%, while the request latency stays mostly unaffected.
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This demo presents a self-operating Kubernetes (K8s) cluster that uses
digital twinning and machine learning to autonomously adapt its Horizontal
Pod Autoscaler (HPA) to workload changes. The demo uses a digital twin of a K8s cluster to gather performance statistics and learn a model for the
workload. With the model, the cluster autonomously adjusts HPA parameters for better performance. The demo illustrates this process and shows that the requested pod seconds decrease by ∼37 \%, while the r...
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