Kubernetes v1.33 Dynamic Resource Allocation: Advanced Hardware Management for AI, ML, and HPC Workloads

Introduction

Kubernetes v1.33 brings significant enhancements to Dynamic Resource Allocation (DRA), a feature quietly evolving into one of the most crucial capabilities for workloads that depend on specialized hardware. Whether you’re running AI/ML training jobs, high-performance computing, or applications needing hardware acceleration, DRA’s improvements address critical gaps in how Kubernetes manages specialized resources like GPUs, FPGAs, high-performance NICs, and vendor-specific devices.

In this in-depth guide, we’ll explore what’s new in Kubernetes v1.33 for DRA, why these changes matter, and how you can begin implementing them in your environment.

What is Dynamic Resource Allocation (DRA)?

Before diving into v1.33’s new features, let’s establish a clear understanding of what DRA is and why it represents such a significant evolution in Kubernetes resource management.

The Problem with the Device Plugin API

Traditionally, Kubernetes relied on the Device Plugin API for hardware management. While functional, this approach had several limitations:

• Node-scoped only: Device management was limited to individual nodes without cluster-wide coordination
• Limited state management: No support for pre-allocation or sharing of device state
• Rigid abstractions: Unable to adapt to complex hardware topologies
• Limited use cases: Couldn’t support advanced scenarios like multi-device allocations or runtime device discovery

The DRA Solution

Dynamic Resource Allocation introduces a clean separation between resource definitions, claims, and drivers, creating a more flexible and powerful abstraction:

This architecture enables:

1. Cluster-wide resource management: Resources are managed at the cluster level, not just per-node
2. Advanced scheduling logic: More sophisticated matching of workloads to hardware
3. Better portability: Workloads become more cloud-agnostic when dealing with specialized hardware
4. Runtime reconfiguration: Hardware can be dynamically configured based on workload needs

What’s New in Kubernetes v1.33?

Driver-Owned Resource Claim Status (Beta)

In v1.33, this feature has been promoted to beta status, providing enhanced visibility into device health and status. It allows node-level drivers to report detailed status information about each allocated device.

apiVersion: resource.k8s.io/v1beta1
kind: ResourceClaim
metadata:
  name: gpu-claim
status:
  driverStatus:
    healthStatus: "healthy"
    temperature: "65C"
    memoryUtilization: "2.3GB/16GB"
    powerState: "normal"

Key Benefits:

• Improved diagnostics: Real-time device health information for troubleshooting
• Enhanced scheduling: Make better decisions based on device health
• Observability: Surface device-specific properties not part of standard Kubernetes resource model

This lays the groundwork for tighter integration between observability tools and the DRA APIs.

New Alpha Features in v1.33

1. Partitionable Devices

Some specialized hardware can be logically divided into smaller functional units. For instance, NVIDIA’s A100 GPUs support Multi-Instance GPU (MIG) technology that partitions a single physical GPU into multiple isolated GPU instances.

The new partitioning alpha feature in v1.33 allows drivers to:

• Advertise and dynamically allocate “slices” of a physical device
• Reconfigure partitioning based on workload demands
• Support mixed workload sizes on a single physical device

apiVersion: resource.k8s.io/v1alpha2
kind: ResourceClass
metadata:
  name: gpu-partitionable
spec:
  driverName: gpu.resource.example.com
  parametersRef:
    apiGroup: gpu.example.com
    kind: GPUParameters
    name: partition-capable
---
apiVersion: gpu.example.com/v1
kind: GPUParameters
metadata:
  name: partition-capable
spec:
  partitionable: true
  partitionSizes:
    - small: "3GB"
    - medium: "6GB"
    - large: "12GB"

Key Benefits:

• Improved hardware utilization: Better sharing of expensive hardware resources
• Reduced scheduling latency: System can allocate partial devices when full ones aren’t available
• Dynamic reconfiguration: Partition sizes can adapt to changing workload needs

This feature is particularly valuable in multi-tenant clusters, edge computing, and anywhere hardware resources are constrained.

2. Device Taints and Tolerations

Borrowing concepts from Kubernetes node taints, v1.33 introduces a mechanism to apply taints to specific devices. This signals that a device should be avoided unless a workload explicitly tolerates that condition.

apiVersion: resource.k8s.io/v1alpha2
kind: ResourceClaimTemplate
metadata:
  name: gpu-claim-template
spec:
  spec:
    resourceClassName: gpu-class
    tolerations:
    - key: "hardware.kubernetes.io/maintenance"
      operator: "Exists"
      effect: "NoSchedule"

Common Use Cases:

• Marking devices undergoing firmware updates or diagnostics
• Indicating degraded but still functional hardware
• Reserving experimental or specialized hardware for specific workloads

This feature brings the same scheduling flexibility to devices that node taints and tolerations brought to Kubernetes nodes.

3. Prioritized List of Acceptable Devices

Many workloads can function with different hardware configurations, albeit with varying performance characteristics. The v1.33 alpha feature allows workloads to submit an ordered list of acceptable device configurations:

apiVersion: resource.k8s.io/v1alpha2
kind: ResourceClaimTemplate
metadata:
  name: gpu-claim-with-fallbacks
spec:
  spec:
    resourceClassName: gpu-class
    devicePreferences:
    - class: "nvidia-a100"
      count: 1
    - class: "nvidia-v100"
      count: 2
    - class: "nvidia-t4"
      count: 4

Benefits:

• Graceful degradation: Workloads can run with alternative resources when preferred ones aren’t available
• Fewer scheduling failures: More flexible matching increases successful scheduling
• Better cluster utilization: Optimizes use of heterogeneous hardware

This is particularly useful for ML training jobs that can parallelize across devices and rendering jobs with flexibility in hardware performance requirements.

Preparing for General Availability

The DRA team is actively preparing for General Availability (GA) in Kubernetes v1.34. Key improvements in v1.33 include:

v1beta2 API Version

This new version simplifies how users define ResourceClaims and ResourceClasses:

# Previous beta1 version
apiVersion: resource.k8s.io/v1beta1
kind: ResourceClaimTemplate
metadata:
  name: gpu-claim
spec:
  spec:
    resourceClassName: gpu-class
    allocationMode: Immediate

# New beta2 version - simplified
apiVersion: resource.k8s.io/v1beta2
kind: ResourceClaimTemplate
metadata:
  name: gpu-claim
spec:
  resourceClassName: gpu-class
  # allocationMode defaults sensibly and can be omitted

Improved RBAC Policies

Enhanced access controls provide tighter security in multi-user environments:

apiVersion: rbac.authorization.k8s.io/v1
kind: Role
metadata:
  name: dra-developer
rules:
- apiGroups: ["resource.k8s.io"]
  resources: ["resourceclaims"]
  verbs: ["get", "list", "create"]
  # Can now restrict by resource class
  resourceNames: ["standard-gpu", "standard-fpga"] 

Driver Upgrade Support

Seamless rolling upgrades for DRA drivers minimize downtime during version changes and feature additions. The v1.33 release includes:

• Coordinated driver rollout mechanisms
• Status preservation during upgrades
• Backward compatibility guarantees

What’s Coming in v1.34?

The roadmap for DRA in v1.34 aims to make the feature Generally Available (GA), which means:

1. No feature gates needed—DRA will work out of the box
2. Currently beta features will become default
3. Alpha features from v1.33 will be promoted to beta
4. DRA will become a core Kubernetes subsystem

Implementation Guide

Prerequisites

• Kubernetes v1.33 or newer
• Feature gate DynamicResourceAllocation=true enabled on:
  • kube-apiserver
  • kube-controller-manager
  • kube-scheduler
  • kubelet
• A compatible device driver installed

Step 1: Enable the Feature Gate

For kubeadm-based clusters, add this to your kubeadm configuration:

apiVersion: kubeadm.k8s.io/v1beta3
kind: ClusterConfiguration
apiServer:
  extraArgs:
    feature-gates: "DynamicResourceAllocation=true"
controllerManager:
  extraArgs:
    feature-gates: "DynamicResourceAllocation=true"
scheduler:
  extraArgs:
    feature-gates: "DynamicResourceAllocation=true"
---
apiVersion: kubelet.config.k8s.io/v1beta1
kind: KubeletConfiguration
featureGates:
  DynamicResourceAllocation: true

Step 2: Install a Compatible Device Driver

Install a device driver that supports DRA. For example, the NVIDIA GPU driver:

helm repo add nvidia https://helm.ngc.nvidia.com/nvidia
helm repo update
helm install nvidia-device-plugin nvidia/gpu-operator \
  --set driver.enabled=true \
  --set devicePlugin.enabled=true \
  --set dra.enabled=true

Step 3: Define a ResourceClass

Create a ResourceClass that defines the type of resource you want to use:

apiVersion: resource.k8s.io/v1beta2
kind: ResourceClass
metadata:
  name: gpu-class
spec:
  driverName: gpu.nvidia.com
  parametersRef:
    apiGroup: gpu.nvidia.com
    kind: GpuClaimParameters
    name: default-parameters
---
apiVersion: gpu.nvidia.com/v1
kind: GpuClaimParameters
metadata:
  name: default-parameters
spec:
  count: 1
  type: "nvidia-tesla-a100"

Step 4: Create a Pod Using DRA

Now create a Pod that requests a GPU using DRA:

apiVersion: v1
kind: Pod
metadata:
  name: gpu-workload
spec:
  containers:
  - name: cuda-container
    image: nvidia/cuda:11.6.2-base-ubuntu20.04
    command: ["nvidia-smi", "-L"]
    resources:
      claims:
      - name: gpu
  resourceClaims:
  - name: gpu
    resourceClaimTemplateName: gpu-claim-template
---
apiVersion: resource.k8s.io/v1beta2
kind: ResourceClaimTemplate
metadata:
  name: gpu-claim-template
spec:
  spec:
    resourceClassName: gpu-class

Best Practices

1. Capacity Planning

• Monitor utilization patterns: Use tools like Prometheus to track device usage
• Set appropriate resource limits: Prevent over-subscription of specialized hardware
• Plan for redundancy: Critical workloads should have device redundancy

2. Performance Optimization

• Batch similar workloads: Schedule similar resource needs together
• Use device affinity: Co-locate related workloads for better data locality
• Consider topology: Hardware location can impact performance (e.g., PCIe topology)

3. Security Considerations

• Implement strict RBAC: Limit who can create ResourceClaims
• Audit device access: Track which workloads use specialized hardware
• Isolate critical devices: Use taints to protect production hardware

Common Pitfalls and Troubleshooting

Issue: ResourceClaims Stuck in “Pending” State

Possible causes:

1. Driver not installed or not running
2. No matching hardware available
3. Feature gates not enabled consistently across components

Troubleshooting:

# Check claim status
kubectl describe resourceclaim my-gpu-claim

# Check driver pods
kubectl get pods -n kube-system | grep driver

# Verify feature gates
kubectl get pods -n kube-system -l component=kube-apiserver -o yaml | grep feature

Issue: Pod Scheduling Failures

Possible causes:

1. ResourceClaim not allocated in time
2. Incompatible node constraints
3. Driver rejecting allocation request

Troubleshooting:

# Check pod events
kubectl describe pod my-gpu-pod

# Check claim allocation
kubectl get resourceclaim my-gpu-claim -o yaml

# Check scheduler logs
kubectl logs -n kube-system -l component=kube-scheduler

Frequently Asked Questions

Is DRA compatible with the existing Device Plugin framework?

Yes, both can coexist. DRA is designed as a more powerful alternative, but existing Device Plugin implementations will continue to function.

Can I migrate from Device Plugin to DRA without downtime?

Yes, you can run both simultaneously during migration. The kubelet supports both mechanisms concurrently.

Does DRA work with autoscaling?

Yes, but you’ll need to configure your cluster autoscaler to be aware of DRA resources to scale nodes appropriately.

How does DRA handle multi-node workloads?

Currently, DRA allocates resources on a per-pod basis. For multi-node workloads, each pod needs its own ResourceClaim.

What happens if a node with allocated devices fails?

The ResourceClaim remains bound to that node until the pod is terminated or the claim is explicitly deleted.

Conclusion

Dynamic Resource Allocation in Kubernetes v1.33 represents a major step forward in how specialized hardware is managed in containerized environments. With features like device partitioning, taints, and prioritized selection, DRA addresses critical gaps in hardware utilization, flexibility, and operational control.

As DRA approaches GA status in v1.34, now is the perfect time to start exploring and testing these features in your environment. The ability to efficiently manage specialized hardware will be increasingly important as AI/ML workloads, edge computing, and high-performance applications continue to grow in Kubernetes environments.

By embracing these new capabilities, you’ll be well-positioned to maximize your hardware investments while providing more flexible, reliable infrastructure to your development teams.

Further Reading

• Kubernetes SIG-Node DRA Design Document
• Kubernetes v1.33 Release Notes
• Dynamic Resource Allocation API Reference

Thanks

A special thank you to everyone in the Kubernetes community who contributed to the development and testing of Dynamic Resource Allocation. Your efforts are making Kubernetes even better for all users.