Improve cluster resource utilizationĬluster operators who run critical services learn over time a rough estimate of the number of nodes that they need in their clusters to achieve high service availability. Within seconds your high priority pods are scheduled, which is critical for latency sensitive services. Moreover, preemption is much faster than adding new nodes to the cluster. With pod priority and preemption you can set a maximum size for your cluster in the Autoscaler configuration to ensure your costs get controlled without sacrificing availability of your service. If you give your critical service the highest priority and your CI/CD and ML workloads lower priority, when your service needs more computing resources, the scheduler preempts (evicts) enough pods of your lower priority workloads, e.g., ML workload, to allow all your higher priority pods to schedule.
When multiple workloads run in the same cluster, the size of your cluster is larger than a cluster that you would use to run only your critical service. For example, you may run your CI/CD pipeline, ML workloads, and your critical service in the same cluster. In this approach, you combine multiple workloads in a single cluster. Adding nodes is not instantaneous and could take minutes before those nodes become available for scheduling.Īn alternative is Pod Priority and Preemption.Adding more nodes to the cluster costs more.However, cluster autoscaler has some limitations and may not work for all users: Kubernetes Cluster Autoscaler is an excellent tool in the ecosystem which adds more nodes to your cluster when your applications need them. Guaranteed scheduling with controlled cost It also provides a way to improve resource utilization in your clusters without sacrificing the reliability of your essential workloads. Pod priority and preemption is a scheduler feature made generally available in Kubernetes 1.14 that allows you to achieve high levels of scheduling confidence for your critical workloads without overprovisioning your clusters. This approach often works, but costs more as you would have to pay for the resources that are idle most of the time. One obvious solution to this problem is to over-provision your cluster resources to have some amount of slack resources available for scale-up situations. When the application is critical for your product, you want to make sure that these new instances are scheduled even when your cluster is under resource pressure.
When a workload is scaled up, more instances of the application get created. It scales your workloads based on their resource usage. Kubernetes is well-known for running scalable workloads.
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