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As GPUs become more powerful, their capacity to handle concurrent workloads increases, presenting new scaling challenges for Kubernetes clusters. In this session, we will share insights and strategies from NVIDIA’s experience right-sizing a Kubernetes control plane, while scaling up to meet business demand.
We will demonstrate how we measure the control plane resource consumption and share techniques and configuration parameters used that improved control-plane performance and scalability, such as: changing golang tunables, the goaway-chance parameter in kube-apiserver and some scheduler configurations. We will also share an often overlooked factor - the volume of YAML per API call. Finally, we will share how we use simulation techniques like KWOK (Kubernetes WithOut Kubelet) to measure new Kubernetes features, like DRA (Dynamic Resource Allocation), for control-plane scalability and performance before we roll it out in production.
Alay is a Senior Software Engineer at Nvidia where he works on cloud gaming service, managing infrastructure for GPU workloads. He is passionate about open source with a focus on Kubernetes and platform engineering.
