Optimizing Batch Workloads in Kubernetes for HPC, AI, and Machine Learning

Introduction

Kubernetes has emerged as a cornerstone of modern cloud-native infrastructure, enabling scalable and flexible application deployment. However, managing batch workloads—critical for high-performance computing (HPC), AI, and machine learning (ML)—requires specialized tools to address unique challenges such as resource contention, latency, and hardware optimization. The Cloud Native Computing Foundation (CNCF) has recognized this need through the Kubernetes Batch Working Group (WG), which focuses on enhancing Kubernetes to better support batch processing. This article explores the technical advancements, core features, and practical applications of the Q project, a key initiative under the WG, to streamline batch workloads in Kubernetes.

Kubernetes Batch Workloads and the CNCF Ecosystem

Batch workloads, such as large-scale simulations, distributed training, and data analytics, demand predictable resource allocation and efficient scheduling. Traditional Kubernetes tools like Cubeflow Jobs and Pots require administrators to predefine templates for storage volumes and mount points, while researchers can submit jobs by selecting templates and providing parameters. This approach simplifies workload creation but lacks native support for HPC and AI-specific requirements.

To bridge this gap, Kubernetes has introduced Slurm-compatible modes to facilitate migration from legacy batch systems. By simulating Slurm’s task metadata and resource allocation strategies (e.g., preemption and decaying aggregated usage), Kubernetes now offers a more seamless transition for users accustomed to Slurm-based workflows.

The Kubernetes Batch Working Group (WG) and Its Objectives

The WG aims to reduce fragmentation in the Kubernetes ecosystem by standardizing APIs and scheduling mechanisms for batch workloads. Key objectives include:

• Supporting diverse workloads: HPC, AI, ML, data analytics, and CI/CD pipelines.
• Enhancing scheduling: Native support for gang scheduling, topology-aware placement, and fair resource sharing.
• Optimizing hardware utilization: Abstracting hardware-specific configurations (e.g., GPUs, TPUs) to enable cross-platform compatibility.

By focusing on these areas, the WG seeks to create a unified framework that balances performance, scalability, and user-friendliness.

Core Features of the Q Project

The Q project is a central component of the WG, offering advanced capabilities for managing batch workloads. Its core features include:

1. Gang Scheduling

• Full-or-nothing semantics: Ensures all tasks in a workload start or stop simultaneously, critical for HPC and distributed training.
• Hardware neutrality: Supports GPUs, TPUs, and custom hardware, enabling deployment across cloud and on-premises environments.

2. Topology-Aware Scheduling

• Avoiding network bottlenecks: Distributes workloads within the same switch to minimize cross-link traffic. For example, separating workloads by color (e.g., red/green) ensures localized communication.
• Dynamic resource allocation: Prioritizes workloads based on historical usage patterns and real-time demand.

3. Fair Sharing and Hierarchical Resource Control

• Preemption mechanisms: Dynamically adjusts resource allocation based on usage ratios. For instance, teams exceeding their quota may have workloads preempted to ensure fairness.
• Hierarchical quotas: Aligns with organizational structures (e.g., department → manager → team) to enable cross-team resource redistribution.
• Automated resource recycling: Frees idle resources at higher organizational levels to improve overall utilization.

4. Job API Advancements

• Job Set: A unified API for managing HPC/AI workloads, supporting strategies like fault tolerance, restart policies, and success handling.
• KJob: Simplifies job creation with template-based YAML generation and integration with Slurm-compatible CLI tools. It supports storage types like NFS, S3, and GCS, automating volume mounting and parameter configuration.

Job API Advancements and QCTL Tool

The QCTL tool, a kubectl plugin, provides daily operations for managing batch workloads, including queue creation, workload listing, and job submission. It integrates seamlessly with Kubernetes, offering a streamlined interface for users.

The Job Set API further enhances usability by abstracting complex configurations, allowing users to define launch/stop strategies, recovery mechanisms, and success conditions. This reduces the overhead of managing individual Kubernetes Jobs, particularly for large-scale HPC and ML workloads.

Challenges and Future Directions

Despite its advancements, the Q project faces several challenges:

• Topology-aware scheduling integration: Deepening collaboration with the Kubernetes scheduler to improve container placement accuracy.
• Multi-cluster management (MultiQ): Optimizing cross-cluster workload distribution while ensuring log accessibility and meeting research requirements.
• API tooling: Expanding the usability of Job Set and KJob through enhanced documentation and extensibility.

Future efforts will also focus on addressing performance bottlenecks in Kubernetes control planes for high-throughput short jobs. For example, long-running tasks should use Deployment or Leader Worker patterns, while short tasks can leverage message queues (e.g., Kafka) to reduce Pod creation overhead.

Conclusion

The Kubernetes Batch Working Group and the Q project represent a significant step toward optimizing batch workloads for HPC, AI, and ML. By introducing gang scheduling, topology-aware placement, and hierarchical resource control, the WG addresses the unique demands of these workloads while maintaining Kubernetes’ flexibility. The integration of Job Set and KJob further simplifies job management, making it easier for users to adopt Kubernetes for batch processing. As the ecosystem evolves, continued collaboration and innovation will be essential to meet the growing needs of data-intensive applications.