Kubernetes v1.35: Introducing Workload Aware Scheduling

Scheduling large workloads is a much more complex and fragile operation than scheduling a single Pod, as it often requires considering all Pods together instead of scheduling each one independently. For example, when scheduling a machine learning batch job, you often need to place each worker strategically, such as on the same rack, to make the entire process as efficient as possible. At the same time, the Pods that are part of such a workload are very often identical from the scheduling perspective, which fundamentally changes how this process should look. There are many custom schedulers adapted to perform workload scheduling efficiently, but considering how common and important workload scheduling is to Kubernetes users, especially in the AI era with the growing number of use cases, it

This Coalition transmission preserves metadata, trust context, and source routing for a Developer Signal item from Kubernetes. The complete article remains with the original publisher.

Content handling is marked as Metadata Only. Use the source article link for the full report, updates, corrections, and publisher-controlled context.