The problem, I suspect is that different places have different needs from a “packing metric” and a scheduler, so there’s almost certainly no one-size-fits-all s

The problem, I suspect is that different places have different needs from a “packing metric” and a scheduler, so there’s almost certainly no one-size-fits-all solution. The big players probably have custom schedulers that are capable of doing all the things we wish the standard K8s scheduler could do (de-scheduling, reevaluating existing pods, purgatory pools, etc) but nobody has the spare time and resources to do. Different places I suspect also want different things from a “packing metric”. I love the idea, but as a counter argument, one previous product my day job had ran customer workloads. These had extremely well-defined min and max resources, and we were primarily interested in packing as many onto a node as we possibly could. Conversely our current product has radically different needs for ideal packing: minimal nodes such that certain critical workloads have the space to expand as needed.