Virtual Kubelets and HPC Integration: Bridging Cloud-Native and High-Performance Computing

Introduction

The integration of Virtual Kubelets with high-performance computing (HPC) systems represents a critical advancement in cloud-native technologies. As organizations seek to leverage the power of supercomputers while adopting modern cloud-native frameworks like Kubernetes, the challenge lies in harmonizing traditional HPC architectures with the dynamic, scalable nature of Kubernetes. This article explores the technical architecture, key features, and practical implications of integrating Virtual Kubelets with HPC systems, emphasizing how this convergence enhances resource utilization, reduces operational complexity, and supports the evolution of cloud-native ecosystems.

Core Concepts and Architecture

Virtual Kubelets and Kubernetes Integration

Virtual Kubelets enable Kubernetes to abstract and manage HPC resources as if they were native cloud nodes. This is achieved through a controller-agent architecture, where the controller runs as a Kubernetes Pod and the agent operates on HPC login nodes. The agent establishes an MTLS-encrypted gRPC tunnel to the controller, allowing seamless communication between Kubernetes and the HPC environment.

HPC vs. Cloud Computing

• Resource Assumptions: Cloud computing assumes infinite resources with limited demand, while HPC operates under limited resources but infinite demand. This fundamental difference necessitates specialized scheduling and resource management strategies.

• Execution Characteristics: Cloud workloads are typically concentrated on a few nodes, whereas HPC workloads span the entire system, requiring efficient parallel processing capabilities.

Kubernetes vs. Slurm Architecture

Both Kubernetes and Slurm are distributed systems with clients, controllers, databases, and node agents. However, Kubernetes centralizes API server management, with components communicating through the API server, while Slurm relies on Bash scripts for command execution, resulting in more complex communication mechanisms. Kubernetes offers flexibility and self-healing capabilities, whereas Slurm excels in scheduling multi-node workloads efficiently.

Key Features and Functionalities

Virtual Kubelets Operational Workflow

1. Tunnel Establishment: The agent creates an MTLS-encrypted gRPC tunnel to the controller.
2. Node Discovery: The controller requests the agent to discover HPC nodes.
3. Virtual Kubelet Deployment: The controller deploys a virtual Kubelet without a physical backend node.
4. Task Forwarding: After deploying a Pod, the controller forwards the task to Slurm for scheduling.
5. Shadow Pod Creation: Slurm splits the task across multiple nodes, generating "shadow Pods".
6. State Synchronization: Kubernetes uses tools like Flux CD to synchronize Pod states, logs, and metadata.

Integration Solutions

• Superetes: The only solution that supports bidirectional synchronization between Kubernetes and HPC, providing a complete node view and cloud-native scheduling capabilities.
• Interlink/HPK: Emerging solutions that enable Kubernetes workloads to be deployed on HPC systems but lack full HPC node information synchronization.
• K Foundry/Slinky Slurm Bridge: Still in development or not publicly available, offering potential future integration paths.

Challenges and Solutions

Multi-Tenancy Isolation

HPC systems traditionally lack namespace isolation (Kernel, User, Process spaces). Solutions include:

• Kubernetes Network Policies: Enforce isolation between workloads.
• Containerization: Leverage container isolation mechanisms to separate tenant environments.

High Availability and Downtime Costs

HPC systems like Lumi incur significant downtime costs (€30M annually, €82K per day). Kubernetes can mitigate this by managing hardware resources and reducing direct HPC system interventions. Multi-cluster integration solutions further enhance fault tolerance and operational resilience.

Future Directions and Roadmap

Phased Integration Goals

1. Base Case: HPC systems directly serve all tenants.
2. Intermediate Stage: Tenants run isolated Slurm clusters within Kubernetes, with Kubernetes exposing HPC hardware resources to Slurm.
3. Final Station: Eliminate native HPC systems entirely, allowing Kubernetes to directly control hardware and integrate with cloud-native batch ecosystems like Flux Framework. This phase also supports cross-cloud and HPC system multi-cluster integration.

Technical Roadmap

• Short-Term: Implement proxy layers for network isolation and basic agent communication.
• Mid-Term: Integrate advanced resource mapping (DRA) for fine-grained hardware control, supporting GPUs and InfiniBand.
• Long-Term: Achieve full cloud-native HPC systems, enabling unified platforms for multi-tenancy, high availability, and AI training workloads.

Conclusion

The integration of Virtual Kubelets with HPC systems represents a transformative step toward cloud-native high-performance computing. By leveraging Kubernetes' scalability and HPC's computational power, organizations can achieve optimized resource utilization, reduced downtime, and enhanced operational flexibility. As the technology matures, the convergence of cloud-native frameworks and traditional HPC will redefine how complex workloads are managed, paving the way for a unified, efficient, and resilient computing ecosystem.