Kubernetes SIG Apps 工作组聚焦：支撑AI与复杂工作负载生命周期管理
随着 Kubernetes 的广泛采用，应用管理已从单纯运行容器转向应对复杂的应用生命周期。现代平台需支持无状态服务、有状态数据库、批处理以及 AI 等工作负载，并在升级、伸缩和基础设施故障时保持高可用。SIG Apps 负责的 Deployments、StatefulSets、Jobs 等核心工作负载组件，构成了整个生态系统部署与运维的基础，该小组正致力于持续优化现代复杂负载的管理体验。
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9月23日2026-09-23
9月22日2026-09-22
Kubernetes v1.37：持久卷声明最后使用时间追踪特性进入 Beta 阶段
Kubernetes v1.37 正式将 PersistentVolumeClaimUnusedSinceTime 特性升级为 Beta 阶段并默认启用。该功能由 PVC 保护控制器直接在每个持久卷声明上增加 Unused 状态条件，无需自定义工具即可识别是否有正在运行的 Pod 引用该存储卷。在大规模集群中，此举可有效追踪并清理 Pod 删除后被遗忘的闲置 PVC 存储资源，提升基础设施资源利用效率。
9月17日2026-09-17
Kubernetes v1.37 强化容器存储安全：引入 Bind 挂载选项与 EmptyDir 权限控制
Kubernetes v1.37 版本引入了重要的存储安全特性，包括 emptyDir 权限模式和 bind 挂载选项。这些新功能基于底层 Linux 存储与权限机制，允许开发者和安全人员直接在 Kubernetes 中实施严格的安全策略，例如禁止跨容器删除文件或禁止从可写卷中执行任意二进制文件，无需复杂的规避手段，从而大幅增强了容器存储的安全性。
9月16日2026-09-16
Kubernetes v1.37发布：Pod级资源管理器正式晋升至Beta阶段
在Kubernetes v1.37中，Pod级资源管理器特性正式升级至Beta阶段（默认保持关闭）。该功能建立在Pod级资源规范之上，使Kubelet的拓扑管理器、CPU管理器和内存管理器能够直接根据Pod级资源声明进行底层硬件调度与分配决策。这一改进解决了此前低延迟敏感型应用为了获取独占NUMA对齐CPU或内存时在资源分配上面临的局限，显著提升了异构与高性能算力节点的调度灵活性。
9月15日2026-09-15
Kubernetes 变化块跟踪 API 升级至 Beta 版本
Kubernetes 中针对 CSI 驱动的变化块跟踪（CBT）功能随着 external-snapshot-metadata 项目 v1.0.0 版本的发布，正式由 Alpha 阶段升级至 Beta 阶段。该功能主要由 CSI SnapshotMetadata gRPC 服务、SnapshotMetadataService CRD 以及 external-snapshot-metadata 边车容器三大核心组件构成。目前 CBT 仅适用于块存储卷，暂不支持文件卷与网络文件共享的变更列表跟踪。
Kubernetes v1.37发布：内存QoS功能正式步入Beta阶段并默认启用
在Kubernetes v1.37中，内存服务质量（Memory QoS）特性已晋升至Beta阶段并默认开启。在运行cgroup v2的Linux节点上，该功能利用内核内存控制器，为容器内存的回收和分配提供更精细的控制与保障。该功能最初于v1.22作为Alpha引入，并在v1.36扩展了分层内存预留支持。此次默认启用有助于集群运维人员进一步优化内存隔离与节点资源利用率。
9月12日2026-09-12
Kubernetes v1.37: Native Histograms Graduates to Beta
I'm excited to announce that native histogram support for Kubernetes metrics is graduating to Beta and is enabled by default in Kubernetes v1.37! Native histograms (previously introduced as Alpha in Kubernetes v1.36 under KEP-5808 ) bring high-resolution, low-cardinality observability to Kubernetes metrics. By adopting Prometheus Native Histograms , Kubernetes components now expose latency and duration metrics with far greater accuracy while significantly reducing telemetry storage and scraping overhead. Why move beyond classic histograms? Since the early days of Kubernetes observability, duration and latency metrics (such as API server reque
9月11日2026-09-11
Kubernetes v1.37: Scheduler Preemption for In-Place Pod Resize (Alpha)
In Kubernetes, resource allocation has historically been a static decision made during a Pod's initial scheduling and placement. With the graduation of the core in-Place Pod resize feature to General Availability in v1.35, application developers and cluster operators gained the powerful ability to dynamically adjust CPU and memory allocations of running containers without incurring disruptive restarts or application downtime. However, in-place resizing introduced a unique resource scheduling gap: if a running Pod requested a resource scale-up that exceeded the host node's allocatable headroom, the Kubelet was forced to mark the request as Def
9月10日2026-09-10
Kubernetes v1.37: Introducing Node Lifecycle Conditions
Kubernetes has many ways to describe what is happening on a Node. Readiness, taints, Pod state, labels, annotations, and provider-specific APIs each expose part of the picture. What has been missing is a shared, Kubernetes-owned way to say that a Node is draining , undergoing maintenance, or undergoing Graceful Node Shutdown . Kubernetes v1.37 introduces five well-known Node conditions that provide that description: DrainInProgress Drained MaintenancePlanned MaintenanceInProgress GracefulNodeShutdownInProgress The new Node lifecycle conditions Condition What it reports DrainInProgress The Node is actively being drained according to the admini
9月9日2026-09-09
Kubernetes v1.37: Advancing Workload-Aware Scheduling
AI/ML and complex batch workloads continue to push the boundaries of Kubernetes scheduling. Following the foundational workload-centric enhancements introduced in previous releases, Kubernetes v1.37 delivers the next major milestone in the Workload-Aware Scheduling (WAS) journey. In this release, the core Workload and PodGroup APIs—enabling gang scheduling—along with Workload-Aware Preemption (WAP) and shared DRA ResourceClaims for PodGroups, all graduate to Beta, solidifying their role in the Kubernetes ecosystem. To address the hierarchical scheduling requirements of modern high-performance distributed workloads, v1.37 introduces the new Co
9月5日2026-09-05
Kubernetes v1.37: KubeletInUserNamespace (aka Rootless mode) Graduates to Beta
Kubernetes v1.37 promotes the KubeletInUserNamespace feature gate to beta. With this feature enabled, all of the node components (kubelet, CRI and OCI runtimes, CNI plugins, and kube-proxy) can run as a non-root user on the host, using a Linux user namespace . This technique is also known as rootless mode . The work started as an experiment in 2018, and was merged into Kubernetes v1.22 (2021) as an alpha feature (Kubernetes Enhancement Proposal KEP-2033 ). This feature should not be confused with user namespaces for pods ( hostUsers: false with the UserNamespacesSupport feature gate, GA since v1.36), which puts pods in user namespaces but sti
9月4日2026-09-04
Kubernetes v1.37: DRA Updates
Kubernetes 1.37 is here and Dynamic Resource Allocation (DRA) keeps pushing past where it started! This release brings DRA Extended Resource support to GA, a milestone the team has been building toward for three straight releases. Several more features graduate to Beta or GA. A fresh batch of alpha features rounds out the release. I'll dive into what's new for DRA in Kubernetes 1.37! What's stable in 1.37 DRA Extended Resource support has graduated to GA. This is the mechanism that lets DRA drivers satisfy requests made through the traditional extended resource API, think example.com/gpu in a Pod spec, without requiring a separate device plug
9月3日2026-09-03
Kubernetes v1.37: Scale Workloads to Zero with HorizontalPodAutoscaler
Kubernetes v1.37 includes API support for horizontal autoscaling of workloads down to zero replicas. This feature is now Beta and enabled by default. A HorizontalPodAutoscaler (HPA) that uses a suitable object metric or external metric can now scale a workload to zero replicas, then bring it back when the metric changes. Before v1.37, you needed an add-on or external component, or you had to enable the Alpha feature gate, to scale from zero. It is now part of core Kubernetes. Scaling to zero removes the last idle Pod from workloads such as queue consumers and batch processors. The savings are largest when each Pod reserves expensive resources
9月2日2026-09-02
Kubernetes v1.37: etcd RangeStream Cuts Memory Use on Large List Reads
I am excited to announce that etcd RangeStream is graduating to beta in Kubernetes v1.37. Paired with etcd v3.7, it reduces the memory the API server and etcd need to read a large collection, and makes peak usage more predictable. The cost of large reads The API server serves most list and watch requests from its in-memory watch cache. Populating that cache requires reading a resource's full state from etcd, at startup and on every re-initialization. For a resource with many objects, or large ones, such as Pods, that read is expensive. The API server already paginated these reads, asking etcd for a fixed number of keys at a time rather than t
9月1日2026-09-01
Kubernetes v1.37: Storage Version Migration Enabled by Default
I am excited that storage version migration (SVM) has graduated to General Availability (GA) in Kubernetes v1.37! After a number of releases of work and testing, the built-in StorageVersionMigration API ( storagemigration.k8s.io/v1 ) and control plane controller are now fully stable and enabled by default across all v1.37 Kubernetes clusters. The problem with stale storage versions In Kubernetes, stored API resources are written using a specific storage version (schema representation). The way Kubernetes interacts with object storage fundamentally requires mutation of a resource in order to ensure that the latest storage version is used for a
8月29日2026-08-29
Kubernetes v1.37: Pod Certificates and Cluster Trust Bundles
Kubernetes brings a wealth of features that make it easy to run your production workloads securely and reliably. While aspects like scheduling, health checks and resource limits are probably at the front of your mind, one other important feature of Kubernetes is production identity — how your workload can authenticate to other systems in order to do its job. Up until now, the primary production identity mechanism built into Kubernetes has been service account JWTs (JSON Web Tokens). These are cryptographically-signed tokens, issued by the control plane of your cluster, that let anyone in the world understand who is calling when your workload
8月28日2026-08-28
Kubernetes v1.37: Metrics API graduates to stable
Kubernetes v1.37 promotes the metrics.k8s.io API to stable ( v1 ). This API provides CPU and memory usage for nodes and Pods, and is the API behind commands such as kubectl top and resource-metrics-based autoscaling. For cluster operators and application developers, this graduation means that the API now has the stability guarantees associated with a Kubernetes stable API. The v1 API has the same resource types and fields as v1beta1 ; this is an API-version graduation, not a change to the metrics that are collected or returned. A long-lived API reaches stable The resource Metrics API was introduced as alpha in Kubernetes v1.6 and became beta
8月26日2026-08-26
Kubernetes v1.37: Garhwal
Editors: Arsh Sharma, Christopher Tineo, Kirti Goyal, Sophia Ugochukwu, Swathi Rao, Troy Connor Similar to previous releases, the release of Kubernetes v1.37 introduces new Stable, Beta, and Alpha features. The consistent delivery of high-quality releases underscores the strength of our development cycle and the vibrant support from our community. This release consists of 67 enhancements. Of those enhancements, 16 have graduated to Stable, 23 have graduated to Beta, 27 are entering Alpha, and 1 is a deprecation/removal. Release theme and logo The theme for Kubernetes v1.37 is Garhwal (गढ़वाल, pronounced gaṛhvāl ), a Himalayan region of Uttara
8月12日2026-08-12
How to Pretty-Print Your Kubernetes YAML as KYAML and Why You'd Want To
YAML has been the standard way to write Kubernetes manifests for years. Every example, tutorial, and configuration file you come across is written in it. The problem isn't that YAML is a bad format. It's that YAML gives you a lot of choices, and not all of them are equally good for writing Kubernetes manifests. Some features make files harder to read, some are easy to misuse and others can lead to surprising behavior. The interesting part is that Kubernetes doesn't actually need most of those features. It only relies on a small subset of YAML. This led to a simple question: if Kubernetes only needs a small part of YAML, why not standardize on
8月4日2026-08-04
Gateway API v1.6: TCPRoute and UDPRoute Graduate to Standard
The Kubernetes SIG Network community is thrilled to share the release of Gateway API v1.6.0 , which was released on June 30th of this year! Gateway API has become the standard for modern, role-oriented, and expressive service networking in Kubernetes. In previous releases, Gateway API established a production-grade foundation for HTTP and TLS layer 7 traffic. With version 1.6.0, Gateway API takes a major step forward by expanding standard layer 4 protocol routing and introducing cleaner API boundaries for experimental innovation. Here is a quick summary of what's new in Gateway API v1.6.0: TCPRoute and UDPRoute Graduate to Standard : Raw L4 T
8月1日2026-08-01
Kubernetes v1.37 Sneak Peek
As we get closer to the release date for Kubernetes v1.37, the project develops and matures, features may be deprecated, removed, or replaced with better ones for the project's overall health. This blog outlines some of the planned changes for the Kubernetes v1.37 release that the release team feels you should be aware of for the continued maintenance of your Kubernetes environment and keeping up to date with the latest changes. The information below reflects the current status of the v1.37 release and may change before the actual release date. Deprecations and removals for Kubernetes v1.37 Kubectl: kubectl run --filename/-f to be deprecated
7月30日2026-07-30
How the controller-runtime Cache Actually Works, and Why Your Controller Does Not Crash the API Server
This article has been revised since it was first published, to correct several significant technical inaccuracies in the original text. Kubernetes has long been the default platform for distributed workloads, and writing your own controller for it is now a matter of a few hours. The common path — Golang, using kubebuilder on top of controller-runtime — gives you a project scaffold, types, and a reconciler. For typical scenarios that is more than enough. But as soon as load grows or the controller starts behaving in ways you did not expect, a whole class of edge cases shows up. Most of them trace back to the same root cause: a fuzzy mental mod
7月15日2026-07-15
Building a Custom Metrics Exporter for Kubernetes
Kubernetes ships with built-in awareness of CPU and memory, but most real-world scaling decisions depend on signals that live entirely outside that narrow window: how many messages are waiting in a queue, how long the last batch job took, how many active WebSocket connections a pod is holding. When the built-in metrics are not enough, a metrics exporter bridges that gap. This post walks through writing one from scratch, packaging it as a container, and wiring it into a cluster so that Prometheus — and ultimately the HorizontalPodAutoscaler — can consume it. What a metrics exporter actually does An exporter is a small HTTP server with a single
7月14日2026-07-14
Operating AI/ML Workloads on Kubernetes: A Headlamp Plugin for Kubeflow
Kubernetes has quietly become the default platform for AI and machine learning. Whether you run notebook servers for data scientists, schedule distributed training jobs, tune hyperparameters, or orchestrate multi-step ML pipelines, those workloads increasingly land on a Kubernetes cluster. Kubeflow is one of the most popular ways to assemble that stack, and it does so the Kubernetes-native way: every capability is exposed as a Custom Resource Definition (CRD). That design is a gift to cluster operators, because it means ML workloads can be observed and managed with the same primitives as everything else in the cluster. But in practice the spe
Kubernetes Dashboard to Headlamp: A Step-by-Step Guide
1. Before you start: know what is changing Kubernetes Dashboard and Headlamp both show what is running in a cluster, but they work differently. When Headlamp runs on the desktop, it uses your existing kubeconfig to connect to one or more clusters and can be extended with plugins. When Headlamp runs inside a cluster, it uses a Kubernetes ServiceAccount to access the API and follow RBAC rules. Kubernetes Dashboard, in contrast, only runs in-cluster and always relies on service account tokens. Understanding these models early helps you choose the right setup and permissions. 1.1 How Kubernetes Dashboard works Dashboard is a web app that runs ins
7月8日2026-07-08
Announcing etcd v3.7.0
This article is a mirror of the original announcement Today, SIG etcd is releasing etcd v3.7.0 , the latest minor release of the popular distributed key-value store and core Kubernetes component. v3.7 ships the long-requested RangeStream feature, delivers several other performance improvements, removes the last remnants of the legacy v2store, and completes a major protobuf overhaul. You can download etcd v3.7.0 here: Source code Binaries Official container images This release also includes new versions of the two core etcd dependencies, bbolt v1.5.0 and raft v3.7.0 . For instructions on installing etcd, see the install documentation . For the
6月27日2026-06-27
Open source maintainership in the age of AI
AI has really changed the game around software development. More people are leveraging AI than ever to contribute patches to projects they use. To me, this is a good thing as more folks will contribute patches rather than fork or not fix them. The main problem is that AI has made generating code fast but there has been very little improvement in maintaining code bases. In this post, we will highlight the ways the Kubernetes community is adapting to the world of AI assisted coding. The first step of this journey was to develop an AI policy. This seems mundane and bureaucratic but there were many PRs that derailed into discussions around AI usa
6月26日2026-06-26
Introducing the Cluster API plugin for Headlamp
Headlamp is an open-source, extensible Kubernetes SIG UI project designed to let you explore, manage, and debug cluster resources directly from a browser. Cluster API (CAPI) is a Kubernetes sub-project that brings declarative, Kubernetes-style APIs to cluster lifecycle management. It lets platform teams provision, upgrade, and manage the lifecycle of Kubernetes clusters using standard Kubernetes objects stored and reconciled in a management cluster. Managing Cluster API resources has historically required raw kubectl commands and deep familiarity with ownership hierarchies. The Headlamp Cluster API plugin brings visual clarity, faster debuggi
Inspect Volcano workloads faster with Headlamp
Volcano is a cloud native batch scheduler for Kubernetes, built for high-performance computing, AI/ML, and other batch workloads. Headlamp is an extensible Kubernetes web UI. With its plugin system, Headlamp can surface APIs and workflows beyond the built-in Kubernetes resources. The Volcano plugin brings core Volcano resources into Headlamp so you can inspect workload state, queue behavior, and gang scheduling details in one place. Kubernetes was originally designed around long-running services, where applications are expected to start and remain available over time. Batch, AI/ML, and HPC workloads often behave differently: jobs arrive dynam
See your serverless: introducing the Headlamp plugin for Knative
Headlamp is an open-source, extensible Kubernetes SIG UI project designed to let you explore, manage, and debug cluster resources. Knative brings serverless workloads to Kubernetes, handling traffic routing, autoscaling, and revision management so teams can deploy and iterate without fighting infrastructure. But operating Knative workloads day-to-day can be difficult, there's still a lot of jumping between the kn CLI, kubectl , and the Kubernetes UI to get a full picture of what's running. We built the Headlamp Knative plugin to bridge that very gap, allowing operators to inspect, understand and act on their workloads all from a single place.
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