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 for kube-scheduler and support them natively. Workload aware scheduling The recent 1.35 release of Kubernetes delivered the first tranche of workload aware scheduling improvements. These are part of a wider effort that is aiming to improve scheduling and management of workloads. The effort will span over many SIGs and releases, and is supposed to gradually expand capabilities of the system toward reaching the north star goal, which is seamless workload scheduling and management in Kubernetes including, but not limited to, preemption and autoscaling. Kubernetes v1.35 introduces the Workload API that you can use to describe the desired shape as well as scheduling-oriented requirements of the workload. It comes with an initial implementation of gang…

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