Delivering Science as a Service: Sci-Orchestra's Cloud-Native Approach to HPC

2026-05-08

AI Summary

This work addresses the growing challenge in modern scientific research where complex infrastructure management, authentication, and deployment processes divert focus from core scientific discovery. To this end, the authors propose Sci-Orchestra, a cloud-native, Kubernetes-based hierarchical orchestration framework that automates experimental workflows through API-driven mechanisms, enabling secure authentication, resource scheduling, and scalable deployment across heterogeneous high-performance computing environments. Sci-Orchestra introduces an autonomous service marketplace to facilitate cross-institutional collaboration and adopts a “black-box” interoperability model that promotes integration of academic, industrial, and research tools while safeguarding intellectual property. By significantly lowering technical barriers, the framework accelerates the transition of research prototypes into production-grade applications, thereby advancing the emerging paradigm of Science as a Service (SciaaS).

Abstract

The increasing complexity of modern computational environments often burdens researchers with infrastructure management, authentication protocols, and container deployments. We present Sci-Orchestra, a layered orchestration framework designed to fully automate experimental workflows, allowing scientists to prioritize scientific discovery over backend operations. By abstracting execution through an API-driven interface, the system assumes responsibility for secure authentication, resource management, and scalable deployment across diverse high-performance computing environments using Kubernetes architectures. A key innovation of Sci-Orchestra is its autonomous marketplace, which serves as a catalyst for cross-institutional collaboration. Through an intuitive user interface, researchers can rapidly deploy and share specialized services via simple selections, eliminating the need for complex installations and technical setups. This modular infrastructure is specifically designed to facilitate industry partnerships as it provides a secure execution environment and allows external collaborators to test and validate proprietary tools without the need for source-code exchange. This ``black-box'' interoperability protects intellectual property while enabling seamless integration into broader scientific pipelines, ultimately accelerating the transition from laboratory prototypes to industrial-scale applications.

Problem

• scientific workflow automation
• infrastructure management
• cross-institutional collaboration
• intellectual property protection
• HPC interoperability

Innovation

• cloud-native orchestration
• autonomous marketplace
• black-box interoperability
• Kubernetes-based HPC
• Science-as-a-Service