• 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 is high time to make workloads a first-class citizen forkube-schedulerand support them natively. • Workload aware scheduling The recent 1.35 release of Kubernetes delivered the first tranche ofworkload aware schedulingimprovements. • These are part of a wider effort that is aiming to improve scheduling and management of workloads.
Article Summaries:
- Kubernetes v1.35 introduces native workload‑aware scheduling, a first‑class feature for multi‑Pod applications. The release adds a new Workload API (scheduling.k8s.io/v1alpha1) that lets users define a group of Pods and attach scheduling policies, such as gang scheduling, which requires all Pods in a group to be scheduled together. This all‑or‑nothing placement prevents partial scheduling and resource waste. The update also improves scheduling of identical Pods through opportunistic batching, speeding up gang scheduling. The changes aim to make Kubernetes a more efficient platform for large workloads, especially in AI and batch‑processing use cases.
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