Federated testbeds enable collaborative research by providing access to diverse resources, including computing power, storage, and specialized hardware like GPUs, programmable switches and smart Network Interface Cards (NICs). Efficiently sharing these resources across federated institutions is challenging, particularly when resources are scarce and costly. GPUs are crucial for AI and machine learning research, but their high demand and expense make efficient management essential. Similarly, advanced experimentation on programmable data plane requires very expensive programmable switches (e.g., based on P4) and smart NICs. This paper introduces SHARY (SHaring Any Resource made easY), a dynamic reservation system that simplifies resource booking and management in federated environments. We show that SHARY can be adopted for heterogenous resources, thanks to an adaptation layer tailored for the specific resource considered. Indeed, it can be integrated with FIGO (Federated Infrastructure for GPU Orchestration), which enhances GPU availability through a demand-driven sharing model. By enabling real-time resource sharing and a flexible booking system, FIGO improves access to GPUs, reduces costs, and accelerates research progress. SHARY can be also integrated with SUP4RNET platform to reserve the access of P4 switches.
@article{arxiv.2501.18840,
title = {Sharing GPUs and Programmable Switches in a Federated Testbed with SHARY},
author = {Stefano Salsano and Andrea Mayer and Paolo Lungaroni and Pierpaolo Loreti and Lorenzo Bracciale and Andrea Detti and Marco Orazi and Paolo Giaccone and Fulvio Risso and Alessandro Cornacchia and Carla Fabiana Chiasserini},
journal= {arXiv preprint arXiv:2501.18840},
year = {2025}
}
Comments
Extended version of a paper accepted to NOMS 2025 conference as a short paper