The greatest challenge in migrating YARN workloads to Kubernetes is perfectly replicating its robust multi-tenant capabilities. This session demonstrates how Volcano uses hierarchical queues as the cornerstone for multi-tenancy, providing granular quota management, fair-share scheduling, and powerful cross-queue elastic preemption.

But the true evolution lies in moving beyond YARN. We will then show how Volcano provides an advanced framework for heterogeneous hardware under a single, unified queuing system. We will dive deep into the scheduling capabilities that the YARN model lacks but modern AI platforms demand, including: tenant-to-hardware affinity, workload-aware resource isolation, and performance-driven topology awareness (for networks, GPUs, etc.).