Uploaded June 2026 | Updated September 2026, 3 weeks ago
NSDI '26 - Enabling AI Network Cross-Layer Design and Operations with Arcadia: A Simulation Platform at Scale
Zhaodong Wang, Satyajeet Singh Ahuja, Xu Zhang, Yuhui Zhang, Max Noormohammadpour, Gregory R. Steinbrecher, Thomas Fuller, Xin Liu, Kevin Quirk, Mikel Jimenez Fernandez, Abhinav Triguna, Yan Cai, and Steve Politis, Meta Platforms; Petr Lapukhov and Naader Hasani, Nvidia; Ying Zhang, Meta Platforms
The rapid evolution of Artificial Intelligence (AI) technology is fueling significant investments by hyperscalers, making AI networks crucial for large-scale training. Understanding the design impacts on AI training requires systematic, cross-layer evaluation. Production experience highlights the need for a robust simulation platform to guide network design and operations. This paper defines the platform requirements, addresses complex design challenges, and shares our experience building Arcadia, a scalable, high-fidelity simulation platform for AI Networks. It operates at the cluster level, focusing on overall cluster performance rather than individual job performance. By using our fast-forwarding, lock free, and synchronization-cost reduction mechanisms, Arcadia achieves scalability and speed, allowing us to faithfully simulate real-world-scale training clusters and plays an important role in guiding Meta's AI network evolution.
View the full NSDI '26 program at usenix.org/conference/nsdi26/technical-sessions
NSDI '26 - Enabling AI Network Cross-Layer Design and Operations with Arcadia: A Simulation Platform at Scale
Zhaodong Wang, Satyajeet Singh Ahuja, Xu Zhang, Yuhui Zhang, Max Noormohammadpour, Gregory R. Steinbrecher, Thomas Fuller, Xin Liu, Kevin Quirk, Mikel Jimenez Fernandez, Abhinav Triguna, Yan Cai, and Steve Politis, Meta Platforms; Petr Lapukhov and Naader Hasani, Nvidia; Ying Zhang, Meta Platforms
The rapid evolution of Artificial Intelligence (AI) technology is fueling significant investments by hyperscalers, making AI networks crucial for large-scale training. Understanding the design impacts on AI training requires systematic, cross-layer evaluation. Production experience highlights the need for a robust simulation platform to guide network design and operations. This paper defines the platform requirements, addresses complex design challenges, and shares our experience building Arcadia, a scalable, high-fidelity simulation platform for AI Networks. It operates at the cluster level, focusing on overall cluster performance rather than individual job performance. By using our fast-forwarding, lock free, and synchronization-cost reduction mechanisms, Arcadia achieves scalability and speed, allowing us to faithfully simulate real-world-scale training clusters and plays an important role in guiding Meta's AI network evolution.
View the full NSDI '26 program at usenix.org/conference/nsdi26/technical-sessions
