It's not clear whether you're using Kubernetes, but the Kubernetes way of dealing with this problem is to declare a memory reservation (i.e., a request) along with the container specification. The amount of the reservation will be deducted from the host's available memory for scheduling purposes, regardless of whether the container actually consumes the reserved amount. It's also a best practice to configure the memory limit to be identical to the reservation, so if the container exceeds the reserved amount, the kernel will terminate it via the OOM killer.

Of course, for this to work, you have to figure out what that reserved amount should be. That is an exercise for the implementer (i.e., you).

See https://kubernetes.io/docs/concepts/configuration/manage-res...

> Attempting to enumerate every resource variable (CPU, IOPS, RSS, Disk, logical count) into a single scoring function feels like an NP-hard trap.

Yeah, don't do that. Figure out what resources your applications need and the declare them, and let the scheduler find the best node based on the requirements you've specified.

> We are trying to write a "God Equation" for our load balancer. We started with row_count, which failed. We looked at disk usage, but that doesn't correlate with RAM because of lazy loading.

A few things come to mind...

First, you're talking about a load balancer, but it's not clear that you're trying to balance load! A good metric to use for load balancing is one whose value is proportional to response latency.

It smells like you're trying to provision resources based on an optimistic prediction of your working set size. Perhaps you need a more pessimistic prediction. It might also be that you're relying too heavily on the kernel to handle paging, when what you really need is a cache tuned for your application that is scan-resistant, coupled with O_DIRECT for I/O.