Organizations running complex workloads on Kubernetes often face
challenges in optimizing pod placement for performance, cost, or resource
efficiency. To address this, Azure Kubernetes Service now offers scheduler
profile configuration with in-tree plugins, available in public preview. This
new capability enables users to customize scheduling behavior and define
workload priorities across specific nodes or the entire cluster. It’s
especially beneficial for advanced scenarios such as data processing, AI/ML, and
cost-optimized workload distribution.
Learn more.