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devopscritical PriorityID: k8s-132-dra-claim-lifecycle-invariant

Kubernetes 1.32 DRA Structured Parameter Claim Lifecycle and CDI Handshake Invariant

Enforces Kubernetes 1.32 Dynamic Resource Allocation (DRA) structured parameters (resource.k8s.io/v1beta1) and strict claim reservation lifecycle patterns. Prevents accelerator (GPU/vPU) allocation race conditions, topology-mismatched scheduling deadlocks, and CDI device handoff corruptions in high-density multi-tenant clusters.

Prohibited Anti-Pattern: Bypassing DRA Structured Parameters with Out-of-Band Driver Mutations or Untracked Ephemeral Device Claims

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---
description: Kubernetes 1.32 DRA Structured Parameter Claim Lifecycle and CDI Handshake Invariant - Enforces Kubernetes 1.32 Dynamic Resource Allocation (DRA) structured parameters (resource.k8s.io/v1beta1) and strict claim reservation lifecycle patterns. Prevents accelerator (GPU/vPU) allocation race conditions, topology-mismatched scheduling deadlocks, and CDI device handoff corruptions in high-density multi-tenant clusters.
globs: deploy/**/*.{yaml,yml}, infra/**/*.{yaml,yml,ts}, k8s/**/*.{yaml,yml}, src/controllers/**/*.{ts,go}, cdk8s/**/*.{ts,js}
alwaysApply: true
verified_by: "agentrules.org"
license: "CC-BY-4.0"
source: "https://agentrules.org/rules/k8s-132-dra-claim-lifecycle-invariant"
---

# Kubernetes 1.32 DRA Structured Parameter Claim Lifecycle and CDI Handshake Invariant

> **Severity**: CRITICAL | **Category**: devops
> **Anti-Pattern Warning**: Bypassing DRA Structured Parameters with Out-of-Band Driver Mutations or Untracked Ephemeral Device Claims

## System Constraints
You are a Principal Software Architect enforcing Kubernetes 1.32 Dynamic Resource Allocation (DRA) invariants. All accelerator resource requests (GPUs, vPUs, FPGAs) must use resource.k8s.io/v1beta1 Structured Parameters via kube-scheduler CEL device selectors instead of vendor-specific controller loops. Ensure resource claims strictly adhere to WaitForFirstConsumer allocation modes and track Pod spec.resourceClaims to eliminate pre-start CDI handshake race conditions.

## Strict Guidelines
- Engineers MUST ALWAYS use 'resource.k8s.io/v1beta1' ResourceClaim and ResourceClaimTemplate APIs with Structured Parameters for accelerator allocations.
- Engineers MUST NOT rely on unmanaged vendor driver controllers to mutate DeviceClass or ResourceClaim statuses out-of-band; allocation evaluation MUST remain within the kube-scheduler CEL engine.
- Workloads requiring topology-aware acceleration MUST ALWAYS configure 'allocationMode: WaitForFirstConsumer' to bind device reservation strictly after pod node affinity and topology spread evaluation.
- Services provisioning multi-tenant accelerator slices MUST validate that Container Device Interface (CDI) spec IDs are uniquely allocated and released via NodePrepareResources/NodeUnprepareResources idempotently.
- Components NEVER attach shared accelerator ResourceClaims across non-cooperating Pod boundaries without validating claim.status.reservedFor reference consistency.

## Bad vs Good Implementations
### Configuring Kubernetes 1.32 DRA ResourceClaim with Structured CEL Selectors

#### Bad Antipattern (AI Common Hallucination)
> **Why it fails**: Legacy extended resource requests ('nvidia.com/gpu') bypass Kubernetes 1.32 DRA structured parameters. They cannot evaluate complex multi-dimensional attributes (memory, compute slice, interconnect topology) in the scheduler, leading to sub-optimal placement and potential CDI driver race conditions during node startup.
```typescript
import * as k8s from '@kubernetes/client-node';

// ANTI-PATTERN: Using deprecated vendor-specific custom CRDs with immediate allocation
// and opaque string parameters, bypassing kube-scheduler 1.32 structured parameter filtering.
export function createBadGpuWorkload(): k8s.V1Pod {
  return {
    apiVersion: 'v1',
    kind: 'Pod',
    metadata: { name: 'training-worker' },
    spec: {
      containers: [{
        name: 'cuda-container',
        image: 'nvcr.io/nvidia/cuda:12.4.0-base-ubuntu22.04',
        // Flawed: Hardcoded device bypass causes race conditions and scheduling deadlocks
        resources: {
          limits: { 'nvidia.com/gpu': '1' }
        }
      }]
    }
  };
}
```

#### Production Standard (Correct)
> **Standard**: Leverages K8s 1.32 resource.k8s.io/v1beta1 Structured Parameters. The CEL selector allows the kube-scheduler to evaluate device suitability without external driver webhook latency. Setting allocationMode to WaitForFirstConsumer ensures the accelerator device is only reserved when the Pod is placed on a compatible node, preventing node-level CDI preparation deadlocks.
```typescript
import { V1Pod } from '@kubernetes/client-node';

// PRODUCTION INVARIANT: K8s 1.32 DRA resource.k8s.io/v1beta1 with Structured Parameters
// and WaitForFirstConsumer to prevent multi-tenant allocation race conditions.
export interface K8s132ResourceClaimTemplate {
  apiVersion: 'resource.k8s.io/v1beta1';
  kind: 'ResourceClaimTemplate';
  metadata: { name: string; namespace: string };
  spec: {
    spec: {
      devices: {
        requests: Array<{
          name: string;
          deviceClassName: string;
          allocationMode: 'WaitForFirstConsumer' | 'ExactCount';
          selectors: Array<{
            cel: { expression: string };
          }>;
        }>;
      };
    };
  };
}

export const gpuClaimTemplate: K8s132ResourceClaimTemplate = {
  apiVersion: 'resource.k8s.io/v1beta1',
  kind: 'ResourceClaimTemplate',
  metadata: { name: 'h100-mig-claim-template', namespace: 'ml-platform' },
  spec: {
    spec: {
      devices: {
        requests: [{
          name: 'mig-slice',
          deviceClassName: 'gpu.nvidia.com',
          allocationMode: 'WaitForFirstConsumer',
          selectors: [{
            cel: {
              expression: "device.attributes['gpu.nvidia.com'].memory >= 40Gi && device.attributes['gpu.nvidia.com'].model == 'H100'"
            }
          }]
        }]
      }
    }
  }
};

export const acceleratedPod: V1Pod = {
  apiVersion: 'v1',
  kind: 'Pod',
  metadata: { name: 'training-worker', namespace: 'ml-platform' },
  spec: {
    resourceClaims: [{
      name: 'gpu-resource',
      resourceClaimTemplateName: 'h100-mig-claim-template'
    }],
    containers: [{
      name: 'cuda-container',
      image: 'nvcr.io/nvidia/cuda:12.4.0-base-ubuntu22.04',
      resources: {
        claims: [{ name: 'gpu-resource' }]
      }
    }]
  }
};
```
Recommended MCP Servers (mcp-serverlist.com)

Equip agent with system tools to verify code for this stack.

Kubernetes Resource Inspector MCP
Inspects running cluster resources, resource claims, resource slices, and device class statuses directly via MCP.
Recommended Agent Skills (askills.dev)

Ready-to-use executable skill functions tailored to this stack.

kubectl DRA Claim Validator
CLI automation skill to validate resource.k8s.io/v1beta1 claim lifecycles, CEL expressions, and CDI driver device state.
Architectural Specification & Invariant Rationale

Architectural Invariant Design: Kubernetes 1.32 DRA Structured Parameter Claim Lifecycle and CDI Handshake Invariant

Kubernetes 1.32 DRA Structured Parameter Claim Lifecycle and CDI Handshake Invariant defines an industrial-grade behavioral standard for devops engineering. Enforces Kubernetes 1.32 Dynamic Resource Allocation (DRA) structured parameters (resource.k8s.io/v1beta1) and strict claim reservation lifecycle patterns. Prevents accelerator (GPU/vPU) allocation race conditions, topology-mismatched scheduling deadlocks, and CDI device handoff corruptions in high-density multi-tenant clusters. Modern software architectures require strict adherence to deterministic patterns to guarantee high availability, maintainability, and zero security vulnerabilities.

Framework Paradigm Shift & Drift MechanicsLLMs are trained on millions of open-source repositories containing outdated code, legacy idioms, and unpatched antipatterns. When coding assistants generate implementations for this stack, they default to common statistical tokens: Bypassing DRA Structured Parameters with Out-of-Band Driver Mutations or Untracked Ephemeral Device Claims Without an explicit system instruction file, developers spend hours debugging silent failures.
Failure Modes & Production Blast Radius

Hazard Analysis: What Breaks Without This Rule

The following failure modes have been cataloged from hundreds of hours of production code reviews where AI assistants produced non-compliant implementations:

Failure ModeAI Symptom (The Anti-Pattern)SeverityProduction Impact & Remedy
AI Hallucination & Deprecation DriftBypassing DRA Structured Parameters with Out-of-Band Driver Mutations or Untracked Ephemeral Device ClaimsCritical
Violating this industrial standard introduces regression bugs, security risks, and unexpected runtime failures in production.
Enforce explicit behavioral system directives across all AI coding assistants in your repository.
Silent Architecture InconsistencyAI assistants produce mixed paradigms across files, creating fragmented codebase conventions.Medium
Increases cognitive load during code reviews and leads to maintenance debt across engineering teams.
Pin project conventions in .cursor/rules, CLAUDE.md, and AGENTS.md so all team agents follow identical guardrails.
Architectural Directives in Detail

Step-by-Step Production Directives

Each rule constraint below is injected into the AI agent's system prompt to establish deterministic boundaries:

1

Engineers MUST ALWAYS use 'resource.k8s.io/v1beta1' ResourceClaim and ResourceClaimTemplate APIs with Structured Parameters for accelerator allocations.

Engineers MUST ALWAYS use 'resource.k8s.io/v1beta1' ResourceClaim and ResourceClaimTemplate APIs with Structured Parameters for accelerator allocations.

Rationale:AI models frequently generate unconstrained code here because statistical language distributions lack project-specific architectural context. Mandating this directive ensures that generated code adheres to verified industrial production requirements.
Enforcement:Integrated into the Agent Pack rule file. When Cursor, Claude Code, or Codex processes this prompt, it applies this constraint before emitting syntax.
2

Engineers MUST NOT rely on unmanaged vendor driver controllers to mutate DeviceClass or ResourceClaim statuses out-of-band

Engineers MUST NOT rely on unmanaged vendor driver controllers to mutate DeviceClass or ResourceClaim statuses out-of-band; allocation evaluation MUST remain within the kube-scheduler CEL engine.

Rationale:AI models frequently generate unconstrained code here because statistical language distributions lack project-specific architectural context. Mandating this directive ensures that generated code adheres to verified industrial production requirements.
Enforcement:Integrated into the Agent Pack rule file. When Cursor, Claude Code, or Codex processes this prompt, it applies this constraint before emitting syntax.
3

Workloads requiring topology-aware acceleration MUST ALWAYS configure 'allocationMode

Workloads requiring topology-aware acceleration MUST ALWAYS configure 'allocationMode: WaitForFirstConsumer' to bind device reservation strictly after pod node affinity and topology spread evaluation.

Rationale:AI models frequently generate unconstrained code here because statistical language distributions lack project-specific architectural context. Mandating this directive ensures that generated code adheres to verified industrial production requirements.
Enforcement:Integrated into the Agent Pack rule file. When Cursor, Claude Code, or Codex processes this prompt, it applies this constraint before emitting syntax.
4

Services provisioning multi-tenant accelerator slices MUST validate that Container Device Interface (CDI) spec IDs are uniquely allocated and released via NodePrepareResources/NodeUnprepareResources idempotently.

Services provisioning multi-tenant accelerator slices MUST validate that Container Device Interface (CDI) spec IDs are uniquely allocated and released via NodePrepareResources/NodeUnprepareResources idempotently.

Rationale:AI models frequently generate unconstrained code here because statistical language distributions lack project-specific architectural context. Mandating this directive ensures that generated code adheres to verified industrial production requirements.
Enforcement:Integrated into the Agent Pack rule file. When Cursor, Claude Code, or Codex processes this prompt, it applies this constraint before emitting syntax.
5

Components NEVER attach shared accelerator ResourceClaims across non-cooperating Pod boundaries without validating claim.status.reservedFor reference consistency.

Components NEVER attach shared accelerator ResourceClaims across non-cooperating Pod boundaries without validating claim.status.reservedFor reference consistency.

Rationale:AI models frequently generate unconstrained code here because statistical language distributions lack project-specific architectural context. Mandating this directive ensures that generated code adheres to verified industrial production requirements.
Enforcement:Integrated into the Agent Pack rule file. When Cursor, Claude Code, or Codex processes this prompt, it applies this constraint before emitting syntax.
Technical Questions & Answers

Frequently Asked Technical Questions

Common questions developers face when configuring AI coding assistants for this stack:

Q:Why is Kubernetes 1.32 DRA Structured Parameter Claim Lifecycle and CDI Handshake Invariant critical for AI coding assistants?

Enforces Kubernetes 1.32 Dynamic Resource Allocation (DRA) structured parameters (resource.k8s.io/v1beta1) and strict claim reservation lifecycle patterns. Prevents accelerator (GPU/vPU) allocation race conditions, topology-mismatched scheduling deadlocks, and CDI device handoff corruptions in high-density multi-tenant clusters. Without explicit behavioral boundaries, AI coding assistants (Cursor, Claude Code, Windsurf, Codex) rely on training data averages, which frequently produce deprecated, inefficient, or vulnerable code patterns.

Q:How does adopting this rule prevent AI hallucinations?

This rule explicitly declares what the AI must avoid (Bypassing DRA Structured Parameters with Out-of-Band Driver Mutations or Untracked Ephemeral Device Claims) and prescribes verified positive examples. Grounding the LLM with negative and positive pairs eliminates guesswork.

Q:Which AI coding environments are supported?

This specification is multi-platform compatible: Cursor (.cursor/rules/*.mdc & .cursorrules), Anthropic Claude Code (CLAUDE.md), Windsurf (.windsurfrules), Codex / Cline (AGENTS.md), and GitHub Copilot (.github/copilot-instructions.md).

Q:Can I combine this rule with other technology stacks?

Yes. Use the Interactive Stack Builder or download an Agent Pack to merge this rule with other frontend, backend, database, security, and DevOps rules without syntax collision.

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