Search arXiv⌕ Search

arXiv · 2610.07644

From ASIC to Fleet: Lessons from Building and Operating a Hyperscaler NIC

Abstract

We describe the operational infrastructure built to deploy and operate fbnic, a custom multi-host NIC, across hundreds of thousands of production hosts at Meta. Vendor multi-host NICs, designed by retrofitting single-host architectures, suffered from shared firmware and buffers that created cascading isolation failures over seven years. fbnic eliminates these through physical isolation, but shifting to in-house hardware shifts the entire operational burden to the hyperscaler. We present a hardware-in-the-loop CI pipeline testing firmware, driver, and kernel cross-products; a unified observability pipeline co-locating NIC and switch counters for cross-layer fault attribution; a driver-first architecture with fewer than ten firmware message types; a targeted firmware upgrade orchestrator at sub-sled granularity; and scoped repair automation confining blast radius to individual host slices. Over ten months, fbnic achieved a 12X reduction in unplanned unavailability, 37% lower mean time to repair, and 2.3X fewer hardware swaps compared to vendor NICs on the same platform.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Prankur Gupta, Alexander Duyck, Jakub Kicinski, Joseph Provine, Neal Peacock, Prabhakaran Ganesan, Rajiv Krishnamurthy, Chen Liu, Akshay Viswakumar, Timothy Vitkin, Jie Meng, Beatriz Padilla Hernandez, Michael Edwards, Andrei Kozlov, Viren Nathan, Tianyi Cui, Joy Chaoyue Xiong, Raul Hormazabal, Mohsin Bashir, Fred Feng, Nathan Walker, Lavin Khandelwal, Matt Maia, Milo Piazza, Mohanraj Thillainayagam, Mahmoud Mehr, Amithash Prasad, Lee Trager. 2026-10-06. From ASIC to Fleet: Lessons from Building and Operating a Hyperscaler NIC. https://arxiv.org/abs/2610.07644

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

Semantic Split Inference for Remote Modulation Recognition

Remote automatic modulation recognition balances sensing-node complexity, reporting cost and accuracy. To address this trade-off, we propose channel-aware semantic split inference: a sensing node sends a semantic report over a noisy link and the edge server completes recognition. In our model, split depth and report length are independent design variables, with end-to-end training through the channel. We compare the resulting design against basic split placements, which run inference at the edge server or at the sensing node, and against a state-of-the-art collaborative scheme. We assess sensing-node model size, computation, latency and energy against recognition accuracy. We show that intermediate splits give the best accuracy-cost trade-off.

cs.NI↗

When Weak Reports Matter: Staged Anchored Fusion for Cooperative UAV Sensing

Local multipath rejection can erase evidence needed for cooperative sensing. We propose staged anchored recovery: preserve strong-only confirmations, then query compatible weak reports using unused strong anchors. For any number of sensing nodes, we prove lossless residual screening and derive corroboration and bidirectional cost laws. In 1,024 five-UAV drops, recovery adds 30 matched targets and three false outputs over strict consensus, matching one-pass anchored confirmation's detection counts while reducing weak uploads by 97.4%. Equal-sized cue/report records yield 4.5% less payload than uploading all eligible reports. Independent validation recovers four additional targets with no observed false outputs.

cs.NI↗

ONDT: A Modular and Virtualized O-RAN Digital Twin for xApp/rApp Closed-Loop Experimentatio

Open Radio Access Network (O-RAN) architectures extend programmability in next-generation cellular networks through Near-Real-Time and Non-Real-Time RAN Intelligent Controllers (RICs), enabling AI/ML-driven xApps and rApps to optimize network behavior dynamically. However, validating automated closed-loop control algorithms in live environments presents significant operational risks and deployment costs. To address this challenge, this paper introduces the O-RAN Digital Twin (ONDT), a modular, virtualized, and cloud-native experimentation framework developed as a 6G enabler within the SNS SUNRISE-6G project. We present the ONDT architecture and its end-to-end experimentation workflows, showing how the framework automates O-RAN-compliant rApp/xApp onboarding, RAN scenario configuration and initialization, Key Performance Metric (KPM) collection, and closed-loop control validation. Experimental validation using Juniper RIC platforms and the Keysight RICtest emulation framework demonstrates ONDT ability to provide an isolated and reproducible environment for KPM monitoring, cell ON/OFF control, automated dataset generation, and rApp/xApp validation.

cs.NI↗