Science at Light Speed: Cloud Native Infrastructure for Astronomy Workloads

Introduction

The exponential growth of data generated by modern astronomical observatories demands infrastructure that can scale, adapt, and ensure reliability. Astronomy workloads, particularly those involving high-performance computing (HPC) and global data collaboration, require a robust foundation to process petabytes of data in real time. The Square Kilometre Array (SKA) project exemplifies this need, leveraging cloud-native infrastructure to manage its unprecedented data pipeline. This article explores how Kubernetes, CNCF tools, and cloud-native principles are applied to address the challenges of astronomy workloads, ensuring performance, scalability, and long-term stability.

Core Concepts and Architecture

Cloud Native Infrastructure Defined

Cloud-native infrastructure refers to the use of containerization, orchestration, and automation to build scalable, resilient systems. It emphasizes microservices, declarative configuration, and continuous integration/continuous deployment (CI/CD) practices. For astronomy workloads, this approach enables dynamic resource allocation, seamless integration of heterogeneous hardware, and global data replication.

Key Components

• Kubernetes: Manages containerized workloads, ensuring efficient resource utilization and isolation. In the SKA project, Kubernetes orchestrates data processing tasks across distributed clusters, including the use of Vcluster to partition supercomputing resources into tenant-specific environments.
• Terraform: Automates infrastructure provisioning, enabling rapid deployment of worker nodes, harvester VMs, and control planes. This reduces manual intervention and ensures consistency across global data centers.
• Open Source Ecosystem: Tools like GitLab, RCD mode, and monitoring solutions are leveraged to avoid custom development, fostering collaboration and reducing maintenance overhead.

High-Performance Computing (HPC) Integration

The SKA project processes data at an astonishing rate—8.9TB per second in South Africa alone. Kubernetes, traditionally associated with cloud environments, faces challenges when integrated with HPC systems due to resource isolation and latency constraints. The SKA team overcomes this by using Vcluster, which abstracts supercomputing resources into Kubernetes-managed pods, enabling efficient parallel processing while maintaining strict isolation between tenants.

Application Case: SKA Data Pipeline

Data Access and Processing

• Science Gateway: Scientists query datasets via a centralized portal, selecting data from specific observatory sites (e.g., Australia’s MeerKAT array). Data can be migrated across SRCE nodes for analysis.
• Jupyter Notebooks: Researchers execute data slicing and transformation tasks, generating new outputs for further analysis.
• Karta Visualization: Tools like Karta enable real-time visualization of SKA simulation data, demonstrating the integration of cloud-native workflows with scientific applications.

Global Data Replication

Data products are replicated globally via 100Gbps links, ensuring accessibility for researchers across time zones. SRCE acts as a unified service layer, aggregating resources from diverse infrastructures (e.g., CSCS supercomputing centers) to support this distributed workflow.

Challenges and Solutions

Kubernetes and HPC Synergy

• Resource Isolation: Vcluster addresses the challenge of isolating HPC resources within Kubernetes clusters, preventing interference between tenant workloads.
• Ecosystem Integration: By reusing existing tools (e.g., GitLab, RCD mode), the SKA team avoids reinventing solutions, accelerating deployment and reducing complexity.

Long-Term Stability

• 50-Year Lifespan Design: The infrastructure must adapt to evolving hardware and software requirements over decades. This necessitates modular design and adherence to CNCF standards for maintainability.
• Reliability Engineering: Automated monitoring and self-healing mechanisms are critical to ensure uninterrupted data processing during the SKA’s 6-month observation cycles.

Conclusion

The SKA project demonstrates how cloud-native infrastructure can revolutionize astronomy workloads by combining Kubernetes, HPC, and open-source tools. By leveraging CNCF technologies, the project achieves scalability, reliability, and global collaboration. For organizations facing similar challenges, adopting cloud-native principles—such as declarative configuration, automated provisioning, and microservices architecture—can unlock new possibilities in handling large-scale scientific data. The future of astronomy lies in infrastructure that evolves as fast as the data it processes.