mlsanityData to 14 Oct 2026

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

arXiv 2605.08396 ↗First posted 8 May 2026cs.CVcited by 0

Not on the map. Why?

Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 4 of its 34 references that are in the map. First posted on arXiv 8 May 2026.

Builds on

4 of 34 references.

References · 4

  1. 2014An introduction to Docker for reproducible research, with examples from the R environmentcited by 26
  2. 2022Exact Gaussian Processes for Massive Datasets via Non-Stationary Sparsity-Discovering Kernelscited by 9
  3. 2017Portable learning environments for hands-on computational instruction: Using container- and cloud-based technology to teach data sciencecited by 3
  4. 2025Foundation Models for Zero-Shot Segmentation of Scientific Images without AI-Ready Datacited by 1

Built on by

Not cited yet.