2025/06/30 by Hongyu Hè, Hè, Hongyu, Minhao Jin +3 · 2 citations
Computer Science · #C.2.3 #Distributed and Parallel Computing Systems #FOS: Computer and information sciences #I.2.3 #I.2.6 #Machine Learning (cs.LG) #Mobile Agent-Based Network Management #Networking and Internet Architecture (cs.NI) #Service-Oriented Architecture and Web Services
paper · pdf · doi:10.48550/arxiv.2506.23964
openalex publication_date 2025/06/30 · openalex created_date 2025/10/09 · openalex updated_date 2026/07/28
Generative ML models are increasingly popular in networking for tasks such as telemetry imputation, prediction, and synthetic trace generation. Despite their capabilities, they suffer from two shortcomings: (i) their output is often visibly violating well-known networking rules, which undermines their trustworthiness; and (ii) they are difficult to control, frequently requiring retraining even for minor changes. To address these limitations and unlock the benefits of generative models for networking, we propose a new paradigm for integrating explicit network knowledge, in the form of first-order logic rules, into ML models used for networking tasks. Rules capture well-known relationships among observed signals, e.g., that increased latency precedes packet loss. While the idea is conceptually straightforward, its realization is challenging: networking knowledge is rarely formalized into rules, and naively injecting rules into ML models often hampers their effectiveness. This paper introduces NetNomos, a multi-stage framework that (i) learns rules directly from data (e.g., measurements); (ii) filters them to select semantically meaningful ones; and (iii) enforces them through collaborative generation between an ML model and a Satisfiability Modulo Theories (SMT) solver. %We evaluate NetNomos both component-wise and end-to-end across four diverse network datasets. We show that NetNomos learns diverse, meaningful rules from four real-world datasets and is 1.6--6.5× more scalable than DuoAI, a state-of-the-art (SOTA) rule-learning method. By enforcing these rules on a generic GPT-2 model, NetNomos achieves performance on par with or even surpassing specialized SOTA systems such as Zoom2Net and NetShare across three networking tasks: telemetry imputation, traffic forecasting, and synthetic data generation.