Deterministic Dynamic Resource Allocation (DRA) Eviction & Claim Deallocation Guardrail

--- description: Deterministic Dynamic Resource Allocation (DRA) Eviction & Claim Deallocation Guardrail - Enforces strict ResourceClaim lifecycles, structured pre-stop termination hooks, and atomic pod disruption barriers during Kubernetes 1.32 Dynamic Resource Allocation (DRA) workload transitions. Guarantees deterministic driver unbinding and deallocation of hardware accelerators (GPUs, LPUs, TPUs) before surge replica scheduling to prevent resource deadlocks and orphaned device claims. globs: deploy/**/*.yaml, k8s/**/*.yaml, helm/**/*.yaml, src/**/*.{ts,tsx,go,py} alwaysApply: true verified_by: "agentrules.org" license: "CC-BY-4.0" source: "https://agentrules.org/rules/k8s-132-dra-atomic-eviction-barrier" --- # Deterministic Dynamic Resource Allocation (DRA) Eviction & Claim Deallocation Guardrail > **Severity**: CRITICAL | **Category**: devops > **Anti-Pattern Warning**: Surge-First Rolling Updates with Inline ResourceClaimTemplates Leading to Accelerator Depletion Deadlocks ## System Constraints You are a Principal Kubernetes Systems Architect specializing in bare-metal accelerator orchestration and Kubernetes 1.32 Dynamic Resource Allocation (DRA). You mandate strict atomic eviction barriers, explicit ResourceClaim lifecycle control, and zero-leak preStop teardown sequences for hardware accelerators (GPUs, LPUs, NPUs). ## Strict Guidelines - Engineers MUST set RollingUpdate maxSurge to 0 when managing capacity-constrained accelerator pools via DRA ResourceClaimTemplates. - Engineers MUST configure explicit preStop lifecycle hooks to flush GPU compute kernels, invoke teardown IPCs, and terminate active driver contexts before SIGKILL escalation. - Workload manifests MUST set dynamic resource claim allocation modes deterministically (e.g., WaitForFirstConsumer) to prevent speculative scheduler lockouts. - Deployments MUST pair PodDisruptionBudgets (maxUnavailable: 1) with terminationGracePeriodSeconds scaled specifically to hardware de-initialization latency. - Engineers MUST NOT rely on default Kubernetes pod termination timelines without validating that the vendor DRA driver has issued PodSchedulingContext unbind and released the hardware slice. ## Bad vs Good Implementations ### Kubernetes 1.32 Deployment with DRA Eviction Barrier & Hardware Release #### Bad Antipattern (AI Common Hallucination) > **Why it fails**: Configuring maxSurge: 1 causes the scheduler to evaluate new pods before older pods yield accelerator allocations. On fixed-capacity clusters, this triggers an immediate Unschedulable deadlock, and the default termination timeline risks leaving hardware states locked on the DRA device plugin. ```yaml apiVersion: apps/v1 kind: Deployment metadata: name: llm-inference-worker spec: replicas: 4 strategy: type: RollingUpdate rollingUpdate: maxSurge: 1 # Anti-pattern: Scheduler attempts to acquire scarce accelerators before releasing old pods maxUnavailable: 0 template: spec: terminationGracePeriodSeconds: 30 # Default grace period causes abrupt driver disconnection containers: - name: vllm image: vllm/vllm-openai:v0.6.0 resources: claims: - name: accelerator-claim resourceClaims: - name: accelerator-claim resourceClaimTemplateName: dynamic-lpu-template ``` #### Production Standard (Correct) > **Standard**: By strictly enforcing maxSurge: 0, the pod is guaranteed to terminate and trigger the DRA controller's unbinding sequence before a new pod requests allocation. The preStop hook ensures clean GPU context termination, avoiding orphaned accelerator memory states. ```yaml apiVersion: apps/v1 kind: Deployment metadata: name: llm-inference-worker spec: replicas: 4 strategy: type: RollingUpdate rollingUpdate: maxSurge: 0 # Invariant: Force deterministic teardown before requesting replacement accelerator maxUnavailable: 1 template: spec: terminationGracePeriodSeconds: 120 # Invariant: Extended grace period for full GPU memory offload and driver flush containers: - name: vllm image: vllm/vllm-openai:v0.6.0 lifecycle: preStop: exec: command: ["/bin/sh", "-c", "drain-traffic && sync-device-state --flush-buffers && sleep 5"] resources: claims: - name: accelerator-claim resourceClaims: - name: accelerator-claim resourceClaimTemplateName: dynamic-lpu-template --- apiVersion: policy/v1 kind: PodDisruptionBudget metadata: name: llm-inference-pdb spec: maxUnavailable: 1 selector: matchLabels: app: llm-inference-worker ```

Architectural Specification & Invariant Rationale

Architectural Invariant Design: Deterministic Dynamic Resource Allocation (DRA) Eviction & Claim Deallocation Guardrail

Deterministic Dynamic Resource Allocation (DRA) Eviction & Claim Deallocation Guardrail defines an industrial-grade behavioral standard for devops engineering. Enforces strict ResourceClaim lifecycles, structured pre-stop termination hooks, and atomic pod disruption barriers during Kubernetes 1.32 Dynamic Resource Allocation (DRA) workload transitions. Guarantees deterministic driver unbinding and deallocation of hardware accelerators (GPUs, LPUs, TPUs) before surge replica scheduling to prevent resource deadlocks and orphaned device claims. 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: Surge-First Rolling Updates with Inline ResourceClaimTemplates Leading to Accelerator Depletion Deadlocks 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 Mode	AI Symptom (The Anti-Pattern)	Severity	Production Impact & Remedy
AI Hallucination & Deprecation Drift	Surge-First Rolling Updates with Inline ResourceClaimTemplates Leading to Accelerator Depletion Deadlocks	Critical	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 Inconsistency	AI 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:

Engineers MUST set RollingUpdate maxSurge to 0 when managing capacity-constrained accelerator pools via DRA ResourceClaimTemplates.

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.