Which component automatically adjusts the number of nodes in a Kubernetes cluster based on workload demands?

Which component automatically adjusts the number of nodes in a Kubernetes cluster based on workload demands?

• A. It scales pods within a single node exclusively.
• B. It schedules workloads on available nodes.
• C. Cluster Autoscaler automatically adjusts the number of nodes in a cluster based on the demands of the workloads.
• D. It provisions storage classes.

Answer: (C)

The main concept here is cluster autoscaling: a component that automatically adjusts the number of nodes in a Kubernetes cluster in response to workload demands. It watches for unschedulable pods or pods needing resources that current nodes can’t provide, and when needed it scales out by adding more nodes through the cloud provider or node pool. When nodes are underutilized and can be removed without impacting running pods, it scales in by terminating those nodes to save costs. This behavior is distinct from scaling the number of pods (which is done by a pod autoscaler) and from the scheduler that decides where pods run, or from storage provisioning that handles dynamic PVs. So, the cluster autoscaler is the component that dynamically changes the node count based on workloads.

The main concept here is cluster autoscaling: a component that automatically adjusts the number of nodes in a Kubernetes cluster in response to workload demands. It watches for unschedulable pods or pods needing resources that current nodes can’t provide, and when needed it scales out by adding more nodes through the cloud provider or node pool. When nodes are underutilized and can be removed without impacting running pods, it scales in by terminating those nodes to save costs. This behavior is distinct from scaling the number of pods (which is done by a pod autoscaler) and from the scheduler that decides where pods run, or from storage provisioning that handles dynamic PVs. So, the cluster autoscaler is the component that dynamically changes the node count based on workloads.