Kubernetes's flat-node model forces us to "simulate" hierarchy with labels, not truly "model" it. This creates severe challenges in heterogeneous clusters: not just operational complexity, but critically, native tools like NodeAffinity and TopologySpreadConstraints cannot treat a rack or node pool as a single unit for capacity assessment or bin-packing.

This session introduces an architecture that elevates infrastructure hierarchy to a "first-class citizen" via the HyperNode API. Its core is a pluggable, automated discovery engine that onboards clusters via existing labels and integrates directly with hardware controllers like NVIDIA UFM.

Using Volcano as a case study, we will show how this mechanism solves complex scheduling challenges that span both network topology and hardware constraints. We believe this is not just an evolution for Volcano, but a proposal to establish physical-world modeling as a universal core abstraction for the CNCF community.