Hybrid Cloud Architectures for Research Computing: Applications and Use Cases

TL;DR

This paper reviews hybrid cloud architectures in research computing, discussing deployment models, tools, challenges, and strategies to improve flexibility, security, and scalability in scientific workflows.

Contribution

It provides a comprehensive overview of hybrid cloud deployment models, tools, and strategies, along with a roadmap for adoption in research environments.

Findings

Hybrid cloud enhances resource efficiency and flexibility.

Strategies for federated computing and multi-cloud orchestration.

Addresses interoperability, security, and reproducibility challenges.

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.