Search arXiv⌕ Search
Home/Archive/arXiv

arXiv · 2609.35742

MINT: Modeling GenAI Impact on Network Traffic

Andrew Nguyen·Samson Kempiak·Agrim Gupta·Koushik Kar·Ish Kumar Jain
View original source ↗Read source document ↗Export metadata ↓

Abstract

Generative AI (GenAI) is becoming a mainstream network workload, yet packet-level simulators lack measure\-ment-driven GenAI traffic models. Currently researchers must approximate GenAI services using traditional sources such as file transfer and video streaming, limiting realistic network evaluation of scheduling and capacity planning. We present MINT, a measurement and modeling framework for GenAI network traffic. Using an isolated net\-work-namespace capture pipeline, we collect client-side traces from three LLM providers across four modalities, cloud and edge servers, and wired and wireless network access points. We find that GenAI modalities exhibit distinct upload/download asymmetry and burst structures that differ from traditional applications. MINT clusters and models these burst regimes and validate empirical burst timing distribution behavior in ns-3 with normalized Wasserstein distances of 2--25\%. Our results also reveal that realistic packet bursts have significantly more variability than constant token generator models. MINT open-sources the first measurement-driven GenAI traffic model for packet-level network simulation.

Explore related subjects

cs.NIcs.SYeess.SY

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Andrew Nguyen, Samson Kempiak, Agrim Gupta, Koushik Kar, Ish Kumar Jain. 2026-09-28. MINT: Modeling GenAI Impact on Network Traffic. https://doi.org/10.1145/3831662.3844175

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

KEEP EXPLORING

Related papers

arXiv2026

GATE: GPU-Accelerated Traffic Engineering for the WAN

Traffic engineering (TE) has become a crucial tool for enforcing routing policy and maintaining operational efficiency in large networks. Existing TE solutions pick an objective function to optimize, aiming to balance (i) allocating traffic optimally with (ii) reacting quickly to demand changes and disruption events. However, as the scale of networks grows, the runtime of the existing optimal solution becomes infeasibly large. The alternative - approximate solvers - result in costly inefficiencies. We present GPU-Accelerated Traffic Engineering (GATE), which achieves the best of both worlds: enabling fast TE runtimes through a highly-parallelizable GPU-compatible decomposition, while iteratively converging to the provably optimal solution. GATE unlocks a unique set of desirable properties: it becomes increasingly parallelizable with network size, supports a wide spectrum of fairness objectives, and offers theoretically guaranteed convergence to the optimal solution and near-optimal convergence within a bounded time. We evaluate GATE on production traces from two large cloud WANs, and show that GATE achieves near-optimal solutions 4-10x faster than state-of-the-art.

cs.NI↗
arXiv2026

Throughput-Optimized Networks at Scale

Data center network design plays a critical role in AI training by supporting scaling to thousands of accelerators. An open problem, designing a near-optimal throughput-oriented network-topology, routing, and collectives-has not been achieved at scale and with broad applicability to physical or implementation constraints. We address this problem with a compelling use-case, Google's TPU v4-8t supercomputer where the topology may be reconfigured to achieve higher All-to-All throughput, supporting large, parallelized AI training. We show that the existing TPU networks leave terabytes per second of throughput on the table and we fill that gap. This paper presents Throughput-Optimized Networks at Scale (TONS), an automated network synthesis framework that meets the high-throughput demands of modern computing. TONS formulates topology synthesis as a linear optimization problem that maximizes a throughput-centric proxy metric, using theory and heuristics to scale to thousands of nodes while producing state-of-the-art network topology performance metrics. We further introduce a state-of-the-art deadlock-free routing scheme compatible with limited virtual channels and optical switch faults, enabling the synthesized topologies to realize their predicted throughput gains in simulation. Evaluating uniform random and All-to-All traffic, TONS networks have a geometric-mean speedups of 2.1x and 1.6x over the best TPU v4-8t torus variants.

cs.NI↗
arXiv2026

SCORAS-MoE: Joint Compression and Resource-Adaptive Deployment of MoE-VLMs in LEO Satellite Networks

Deploying large vision-language models (VLMs) onboard satellites enables onboard data processing and reduces raw data downlink. However, onboard inference faces two resource challenges. Limited onboard memory and energy require model compression and distributed deployment. Dynamic resource availability requires fast deployment decisions as illumination, battery levels, and communication conditions change. We present SCORAS-MoE, a joint compression and deployment framework for mixture-of-experts (MoE) VLMs in low Earth orbit (LEO) satellite networks. To address limited resources, SCORAS-MoE measures the perturbation of the routed MoE output caused by low-rank approximation, assigns higher ranks to more sensitive experts, and distributes compressed model shards across satellites for cooperative inference. The compressed models yield profiles of measured accuracy and inference energy. To adapt to dynamic resources, the online scheduler selects profile compositions and shard placements in each slot. For each candidate composition, it reduces placement to a minimum-cost assignment problem solved by the Hungarian algorithm, while enumerating the compositions yields the optimal deployment for the current-slot objective. Experiments on Qwen3-VL-30B-A3B-Instruct show that allocating ranks based on output perturbation is particularly effective under aggressive compression, with an absolute gain of $3.7\%$ in mean accuracy over uniform rank allocation when expert projections retain $30\%$ of their original parameters. The fixed-profile scheduler achieves higher throughput with fewer service switches and lower battery impact than the evaluated proximal policy optimization (PPO) and evolutionary baselines, with respective speedups of $8.7\times$ and $183.5\times$. Adaptive profile selection further improves the balance between service quality and energy use.

cs.NI↗