I wonder if k8s' bin-packing features would help here. The graphs seems to validate my general assumption that large-load tasks just suck at scaling whereas sm

I wonder if k8s' bin-packing features would help here. The graphs seems to validate my general assumption that large-load tasks just suck at scaling whereas small-load tasks can be horizontally scaled easier without falling over. The general assumption being that for most applications, if you ignore everything else about an operation and assume a somewhat random distribution of load, smaller-load services use up more available resources on average than a single large-load service. That's just been an assumption in my head, I can't remember any data to back that up. Back in the day when I worked on a large-traffic internet site, we tried jiggering with the scheduler and other kernel tweaks, and in the end we literally just routed certain kinds of requests to certain machines and said "these requests need loads of cache" (memcache, local disk, nfs cache, etc) and "these requests need fast io" and "these requests need a ton of cpu". It was "dumb" engineering but it worked out.