Hi all, We run hundreds of Flink pipelines across several shared GKE clusters and experience significant cluster overhead. By cluster overhead, we mean allocatable CPU and memory available on active nodes that remain unrequested but cannot be reclaimed because the nodes still host pods.
This comes mainly from two sources: - TaskManager CPU/memory request ratios do not always align with our machine shapes, leaving capacity stranded when either CPU or memory is exhausted first. - Deployments and autoscaling gradually scatter TaskManagers across nodes. Because TaskManagers are bare pods, GKE cannot evict them for consolidation, and Kubernetes does not proactively repack them. As a result, partially occupied nodes can remain running indefinitely despite substantial aggregate free capacity. Have others operating large shared Flink clusters encountered this? How do you manage resource-shape mismatch and fragmentation? Thanks, Yanis
