Abstract: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: \emph{(i)} their output is often visibly violating well-known networking rules, which undermines their trustworthiness; and \emph{(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 \emph{(i)} learns rules directly from data (e.g., measurements); \emph{(ii)} filters them to select semantically meaningful ones; and \emph{(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$\times$ 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.
| Comments: | Published at NSDI '26; Code available at this https URL and this https URL |
| Subjects: | Networking and Internet Architecture (cs.NI); Machine Learning (cs.LG) |
| ACM classes: | C.2.3; I.2.6; I.2.3 |
| Cite as: | arXiv:2506.23964 [cs.NI] |
| (or arXiv:2506.23964v3 [cs.NI] for this version) | |
| https://doi.org/10.48550/arXiv.2506.23964 arXiv-issued DOI via DataCite |
From: Hongyu Hè [view email]
[v1]
Mon, 30 Jun 2025 15:36:22 UTC (1,610 KB)
[v2]
Sat, 4 Oct 2025 02:36:51 UTC (988 KB)
[v3]
Wed, 29 Apr 2026 19:09:55 UTC (3,253 KB)
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