Data Locality: The data locality requirement of data-intensive applications is similar to the previous use case.For example, it’s a good practice to run a web server on the same machine as an in-memory cache service or database. Pods colocation and codependency: In a microservices setting or a tightly coupled application stack, certain pods should be collocated on the same machine to improve performance, avoid network latency issues, and connection failures.Thus, the best practice for any resource-aware K8s cluster management is to assign pods to the nodes with the right hardware. For example, pods running ML jobs require performant GPUs instead of CPUs, while Elasticsearch pods would be more efficient on SSDs than HDDs. Running pods on nodes with dedicated hardware: Some Kubernetes apps may have specific hardware requirements.These are some of the most common scenarios when advanced pod scheduling would be desirable: In the production Kubernetes setting, customizing how pods are scheduled to nodes is a necessity. Use Cases for Manual Pod-to-Node Scheduling This will be especially helpful to app engineers and K8s administrators looking to implement advanced application deployment patterns involving data locality, pod co-location, high availability, and efficient resource utilization of their K8s clusters. In this article, I’ll review some of the use cases for advanced pod scheduling in Kubernetes as well as best practices for implementing it in real-world situations. This is when advanced pod scheduling should be considered. However, in certain use cases Kubernetes administrators want to schedule pods to specific nodes according to other constraints. In many scenarios, scheduling pods based on resource constraints is a desired behavior. Feasible nodes are then scored to find the best candidate for the pod placement. ![]() ![]() The default behavior of this component is to filter nodes based on the resource requests and limits of each container in the created pod. In Kubernetes, the task of scheduling pods to specific nodes in the cluster is handled by the kube-scheduler. Guest post by Ben Hirschberg, VP R&D & Co-founder, ARMO
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