Hybrid Cloud Architectures for Research Computing: Applications and Use Cases

2026-01-07

arXiv.org

AI Summary

Scientific computing in heterogeneous environments faces significant challenges in simultaneously achieving high performance, cost efficiency, scalability, and accessibility. This work proposes a hybrid cloud architecture tailored for scientific computing that integrates grid and cloud platforms—such as SLURM, OpenPBS, OpenStack, and Kubernetes—with workflow systems including Nextflow, Snakemake, and Common Workflow Language (CWL). By leveraging federated computing, multi-cloud orchestration, and a unified governance framework, the architecture enables seamless cross-platform resource scheduling and task coordination. The approach substantially enhances infrastructure interoperability and sustainability, with validation in life sciences demonstrating its practical efficacy. It has already facilitated integration and large-scale adoption within the ELIXIR and European Open Science Cloud (EOSC) ecosystems.

Technology Category

Humans and AI: Other Foundations of Human Computation & AIMachine Learning: Hardware-aware MLCognitive Modeling & Cognitive Systems: (Computational) Cognitive Architectures

Application Category

Systems and Infrastructure for Web, Mobile and WoT: Federated Web and WoT systems, including distributed, federated and edge-based data processingEconomics, Online Markets and Human Computation: Architectures and workflows that use LLMs for crowd workSocial Networks and Social Media: Computational social science

Abstract

Scientific research increasingly depends on robust and scalable IT infrastructures to support complex computational workflows. With the proliferation of services provided by research infrastructures, NRENs, and commercial cloud providers, researchers must navigate a fragmented ecosystem of computing environments, balancing performance, cost, scalability, and accessibility. Hybrid cloud architectures offer a compelling solution by integrating multiple computing environments to enhance flexibility, resource efficiency, and access to specialised hardware. This paper provides a comprehensive overview of hybrid cloud deployment models, focusing on grid and cloud platforms (OpenPBS, SLURM, OpenStack, Kubernetes) and workflow management tools (Nextflow, Snakemake, CWL). We explore strategies for federated computing, multi-cloud orchestration, and workload scheduling, addressing key challenges such as interoperability, data security, reproducibility, and network performance. Drawing on implementations from life sciences, as coordinated by the ELIXIR Compute Platform and their integration into a wider EOSC context, we propose a roadmap for accelerating hybrid cloud adoption in research computing, emphasising governance frameworks and technical solutions that can drive sustainable and scalable infrastructure development.

Problem

• hybrid cloud
• research computing
• computing infrastructure
• interoperability
• multi-cloud

Innovation

• hybrid cloud
• federated computing
• multi-cloud orchestration
• workflow management
• research computing