Latest Kubernetes Update Makes Platform Simpler to Scale

The Technical Oversight Committee (TOC) for Kubernetes has released an update, codenamed Penelope, that advances efforts to improve dynamic resource allocation (DRA) in addition to deprecating the previous method used for scaling clusters.

Frederico Muñoz, release lead of Kubernetes 1.32 and a cloud architect for SAS Institute, said the DRA capabilities, now available in beta, will make it simpler to dynamically scale workloads without having to restart Kubernetes clusters. In addition, it will eliminate the need to rely on a third-party plugin to manage that process as DRA becomes a native Kubernetes feature, he noted.

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That capability will prove to be especially critical as more specialized hardware, in the form of graphical processor units (GPUs), field programmable gate arrays (FPGAs) and network adapters are added to clusters, said Muñoz.

The goal is to replace the preview DRA approach, introduced in Kubernetes 1.26, with an approach that scales vertically and horizontally more efficiently, he noted.

In total, this release adds 44 enhancements in total, with 13 having graduated to stable, 12 entering Beta, and 19 representing Alpha capabilities that are being vetted for the first time.

Capabilities that are now stable enough to use in production environments include support for customer resource field selectors that now mirror the functionality of other Kubernetes objects; an ability to dynamically size memory-backed volumes based on Pod resource limits; inclusion of the node name in the service account token; support for multiple authorizers of the application programming interface (API) server; and an ability to automatically delete PersistentVolumeClaims (PVCs) created by StatefulSets.

Exiting features that are now available in beta include an API for managing job synchronization; an ability to specify which endpoints are allowed to make anonymous requests; a function that enables more accurate queuing using kube-scheduler; simpler recovery from a failed volume expansion; the addition of a VolumeGroupSnapshot API; and an ability to use label and field selectors to authorize access to nodes and pods.

New alpha features being tested include an asynchronous preemption capability for Kubernetes Scheduler; support for mutating admission to make defining more granular policies simpler using the Common Expression Language (CEL); an ability to set resource request and limits at the pod level; an ability to set a zero-second sleep duration for the PreStop lifecycle hook in Kubernetes; a standard interface for network data; tools to better debug endpoints; and more graceful shutdowns of Kubernetes cluster running on Windows servers.

Finally, the flowcontrol.apiserver.k8s.io/v1beta3 API version of FlowSchema and PriorityLevelConfiguration has been removed in favor of a new API implementation and a system watchdog capability now makes it possible to restart the kubelet when its health check fails, while also limiting the maximum number of restarts within a given time period.

Each organization will need to decide for themselves when it makes sense to upgrade to Kubernetes 1.32. Others might simply wait until the provider of the distribution of Kubernetes they rely on adds support.

Regardless of how organizations deploy Kubernetes, the one thing that is clear is that from an operational perspective, the platform continues to mature in a way that makes it less complicated for DevOps and platform engineering teams to manage at scale.