feat(blog): add KCD Beijing 2026 HAMi-DRA recap article

blog/kcd-beijing-2026-dra-gpu-scheduling/index.md

@@ -0,0 +1,292 @@
+---
+title: "From Device Plugin to DRA: GPU Scheduling Paradigm Upgrade and HAMi-DRA Practice Review"
+date: "2026-03-23"
+description: "This post reviews HAMi community's technical sharing at KCD Beijing 2026, exploring the paradigm shift from Device Plugin to DRA in GPU scheduling, and HAMi-DRA's practical experience and performance optimization."
+tags: ["KCD", "DRA", "GPU", "Kubernetes", "AI", "scheduling"]
+authors: [hami_community]
+---
+
+[KCD Beijing 2026](https://community.cncf.io/events/details/cncf-kcd-beijing-presents-kcd-beijing-vllm-2026/) was one of the largest Kubernetes community events in recent years.
+
+**Over 1,000 people registered, setting a new record for KCD Beijing.**
+
+The HAMi community not only gave a technical talk but also set up a booth, engaging deeply with developers and enterprise users from the cloud-native and AI infrastructure fields.
+
+The topic of this talk was:
+
+> **From Device Plugin to DRA: GPU Scheduling Paradigm Upgrade and HAMi-DRA Practice**
+
+This article combines the on-site presentation and slides for a more complete technical review. Slides download: [GitHub - HAMi-DRA KCD Beijing 2026](https://github.com/Project-HAMi/community/blob/main/talks/01-kcd-beijing-20260323/KCD-Beijing-2026-GPU-Scheduling-DRA-HAMi-Wang-Jifei-James-Deng.pdf).
+
+<!-- truncate -->
+
+## HAMi Community at the Event
+
+The talk was delivered by two core contributors of the HAMi community:
+
+- Wang Jifei (Dynamia, HAMi Approver, main HAMi-DRA contributor)
+- James Deng (Fourth Paradigm, HAMi Reviewer)
+
+They have long focused on:
+
+- GPU scheduling and virtualization
+- Kubernetes resource models
+- Heterogeneous compute management
+
+At the booth, the HAMi community discussed with attendees questions such as:
+
+- Is Kubernetes really suitable for AI workloads?
+- Should GPUs be treated as "scheduling resources" rather than "devices"?
+- How to introduce DRA without breaking the ecosystem?
+
+## Event Recap
+
+![Main conference hall](/img/kcd-beijing-2026/keynote.jpg)
+
+![Attendee registration](/img/kcd-beijing-2026/register.jpg)
+
+![Attendees visiting the HAMi booth](/img/kcd-beijing-2026/booth.jpg)
+
+![Volunteers stamping for attendees](/img/kcd-beijing-2026/booth2.jpg)
+
+![Wang Jifei presenting](/img/kcd-beijing-2026/wangjifei.jpg)
+
+![James Deng presenting](/img/kcd-beijing-2026/james.jpg)
+
+## GPU Scheduling Paradigm is Changing
+
+The core of this talk is not just DRA itself, but a bigger shift:
+
+> **GPUs are evolving from "devices" to "resource objects".**
+
+## 1. The Ceiling of Device Plugin
+
+The problem with the traditional model is its limited expressiveness:
+
+- Can only describe "quantity" (`nvidia.com/gpu: 1`)
+- Cannot express:
+  - Multi-dimensional resources (memory / core / slice)
+  - Multi-card combinations
+  - Topology (NUMA / NVLink)
+
+👉 This directly leads to:
+
+- Scheduling logic leakage (extender / sidecar)
+- Increased system complexity
+- Limited concurrency
+
+## 2. DRA: Leap in Resource Modeling
+
+DRA's core advantages are:
+
+- **Multi-dimensional resource modeling**
+- **Complete device lifecycle management**
+- **Fine-grained resource allocation**
+
+Key change:
+
+> **Resource requests move from Pod fields → independent ResourceClaim objects**
+
+## Key Reality: DRA is Too Complex
+
+A key slide in the PPT, often overlooked:
+
+### 👉 DRA request looks like this
+
+```yaml
+spec:
+  devices:
+    requests:
+    - exactly:
+        allocationMode: ExactCount
+        capacity:
+          requests:
+            memory: 4194304k
+            count: 1
+```
+
+And you also need to write a CEL selector:
+
+```yaml
+device.attributes["gpu.hami.io"].type == "hami-gpu"
+```
+
+### Compared to Device Plugin
+
+```yaml
+resources:
+  limits:
+    nvidia.com/gpu: 1
+```
+
+👉 The conclusion is clear:
+
+> **DRA is an upgrade in capability, but UX is clearly degraded.**
+
+## HAMi-DRA's Key Breakthrough: Automation
+
+One of the most valuable parts of this talk:
+
+### 👉 Webhook Automatically Generates ResourceClaim
+
+HAMi's approach is not to have users "use DRA directly", but:
+
+> **Let users keep using Device Plugin, and the system automatically converts to DRA**
+
+### How it works
+
+Input (user):
+
+```yaml
+nvidia.com/gpu: 1
+nvidia.com/gpumemory: 4000
+```
+
+↓
+
+Webhook conversion:
+
+- Generate ResourceClaim
+- Build CEL selector
+- Inject device constraints (UUID / GPU type)
+
+↓
+
+Output (system internal):
+
+- Standard DRA objects
+- Schedulable resource expression
+
+### Core value
+
+> **Turn DRA from an "expert interface" into an interface ordinary users can use.**
+
+## DRA Driver: Real Implementation Complexity
+
+DRA driver is not just "registering resources", but full lifecycle management:
+
+### Three core interfaces
+
+- Publish Resources
+- Prepare Resources
+- Unprepare Resources
+
+### Real challenges
+
+- `libvgpu.so` injection
+- `ld.so.preload`
+- Environment variable management
+- Temporary directories (cache / lock)
+
+👉 This means:
+
+> **GPU scheduling has entered the runtime orchestration layer, not just simple resource allocation.**
+
+## Performance Comparison: DRA is Not Just "More Elegant"
+
+A key benchmark from the PPT:
+
+### Pod creation time comparison
+
+- HAMi (traditional): up to ~42,000
+- HAMi-DRA: significantly reduced (~30%+ improvement)
+
+👉 This shows:
+
+> **DRA's resource pre-binding mechanism can reduce scheduling conflicts and retries**
+
+## Observability Paradigm Shift
+
+An underestimated change:
+
+### Traditional model
+
+- Resource info: from Node
+- Usage: from Pod
+- → Needs aggregation, inference
+
+### DRA model
+
+- ResourceSlice: device inventory
+- ResourceClaim: resource allocation
+- → **Resource perspective is first-class**
+
+👉 The change:
+
+> **Observability shifts from "inference" to "direct modeling"**
+
+## Unified Modeling for Heterogeneous Devices
+
+A key future direction from the PPT:
+
+> **If device attributes are standardized, a vendor-agnostic scheduling model is possible**
+
+For example:
+
+- PCIe root
+- PCI bus ID
+- GPU attributes
+
+👉 This is a bigger narrative:
+
+> **DRA is the starting point for heterogeneous compute abstraction**
+
+## Bigger Trend: Kubernetes is Becoming the AI Control Plane
+
+Connecting these points reveals a bigger trend:
+
+### 1. Node → Resource
+
+- From "scheduling machines"
+- To "scheduling resource objects"
+
+### 2. Device → Virtual Resource
+
+- GPU is no longer just a card
+- But a divisible, composable resource
+
+### 3. Imperative → Declarative
+
+- Scheduling logic → resource declaration
+
+👉 Essentially:
+
+> **Kubernetes is evolving into the AI Infra Control Plane**
+
+## HAMi's Position
+
+HAMi's positioning is becoming clearer:
+
+> **GPU Resource Layer on Kubernetes**
+
+- Downward: adapts to heterogeneous GPUs
+- Upward: supports AI workloads (training / inference / Agent)
+- Middle: scheduling + virtualization + abstraction
+
+HAMi-DRA:
+
+> **is the key step aligning this resource layer with Kubernetes native models**
+
+## Community Significance
+
+Another important point from this talk:
+
+- Contributors from different companies collaborated
+- Validated in real production environments
+- Shared experience through the community
+
+This is the way HAMi has always insisted on:
+
+> **Promoting AI infrastructure through community, not closed systems**
+
+## Summary
+
+The real value of this talk is not just introducing DRA, but answering a key question:
+
+> **How to turn a "correct but hard to use" model into a system you can use today?**
+
+HAMi-DRA's answer:
+
+- Don't change user habits
+- Absorb DRA capabilities
+- Handle complexity internally

i18n/zh/code.json

@@ -675,5 +675,17 @@
   "theme.SearchModal.footer.backToSearchText": {
     "message": "Back to search",
     "description": "The back to search text for footer"
+  },
+  "theme.hami.blog.meta.unknownAuthor": {
+    "message": "HAMi 社区",
+    "description": "Default author name for blog posts when no authors are specified"
+  },
+  "theme.hami.blog.meta.authorLabel": {
+    "message": "作者",
+    "description": "Label for blog post author metadata"
+  },
+  "theme.hami.blog.meta.publishedLabel": {
+    "message": "发布时间",
+    "description": "Label for blog post publish date metadata"
   }
 }