KubeCon + CloudNativeCon North America 2024

KubeStellar (KS) is an open-source Kubernetes multi-cluster workload configuration management system that can be used to manage AI workloads in multi-cluster environments. Hence, understanding KS performance is crucial especially when managing resource intensive AI workloads. In this talk, we will present our experience in analyzing the performance metrics of KS across several dimensions of scalability (e.g., number of bindingPolicies, workload description spaces and number of managed remote clusters) and challenges that arise when conducting performance experiments in a multi-cluster environment. Our insights will demonstrate the utility of benchmarking the performance of a multi-cluster Kubernetes workload management application. Additionally, in this talk, we will demonstrate the usefulness of using several opensource tools such as clusterloader2, kube-burner & kwok to evaluate the performance of multi-cluster Kubernetes management applications.