Software-Defined Networks and 5G

Derniers papiers

• From Automated to Autonomous: Hierarchical Agent-native Network Architecture (HANA)

  Binghan Wu, Shoufeng Wang, Yunxin Liu, Ya-Qin Zhang, Joseph Sifakis, Ye Ouyang · 22 mai 2026

  Realizing Level 4/5 Autonomous Networks (AN) demands a shift from static automation to agent-native intelligence. Current operations, reliant on rigid scripts, lack the cognitive agency to handle off-nominal conditions. To address this, this letter proposes a hierarchical multi-agent reference archi…

• TwinRouterBench: Fast Static and Live Dynamic Evaluation for Realistic Agentic LLM Routing

  Pei Yang, Wanyi Chen, Tongyun Yang, Pengbin Feng, Jiarong Xing, Wentao Guo, Yuhang Yao, Yuhang Han, Hanchen Li, Xu Wang, Zeyu Wang, Jie Xiao, Anjie Yang, Liang Tian, Lynn Ai, Eric Yang, Tianyu Shi · 20 mai 2026

  LLM routing matters most in long-horizon applications such as coding agents, deep research systems, and computer-use agents, where a single user request triggers many model calls. Routing each call to the cheapest sufficient model can cut costs without sacrificing quality, yet existing router benchm…

• Silent Neuron Theory and Plasticity Preservation for Deep Reinforcement Learning in Adaptive Video Streaming

  Zhiqiang He, Zhi Liu · 15 mai 2026

  Adaptive video streaming optimizes Quality of Experience (QoE) metrics by selecting appropriate bitrates according to varying network bandwidth and user demands. In practice, however, real-world network bandwidth often exhibits heterogeneity relative to training environments. Current methods predomi…

• Dense vs Sparse Pretraining at Tiny Scale: Active-Parameter vs Total-Parameter Matching

  Abdalrahman Wael · 14 mai 2026

  We study dense and mixture-of-experts (MoE) transformers in a tiny-scale pretraining regime under a shared LLaMA-style decoder training recipe. The sparse model replaces dense feed-forward blocks with Mixtral-style routed experts. Dense baselines are modestly width-resized to tightly match either ac…

• NeuroRisk: Physics-Informed Neural Optimization for Risk-Aware Traffic Engineering

  Yingming Mao, Ximeng Liu, Jingyi Cheng, Xiyuan Liu, Jiashuai Liu, Yike Liu, Zhen Yao, Yuzhou Zhou, Siyuan Feng, Qiaozhu Zhai, Shizhen Zhao · 14 mai 2026

  In production Wide-Area Networks (WANs), correlated failures dominate availability losses, forcing operators to reserve large safety margins that leave substantial capacity underutilized. Achieving high utilization under strict availability targets therefore requires risk-aware Traffic Engineering (…

• Generalization Bounds of Emergent Communications for Agentic AI Networking

  Yong Xiao, Jingxuan Chai, Guangming Shi, Ping Zhang · 12 mai 2026

  The evolution of 6G networking toward agentic AI networking (AgentNet) systems requires a shift from traditional data pipelines to task-aware, agentic AI-native communication solutions. Emergent communication, a novel communication paradigm in which autonomous agents learn their own signaling protoc…

• Hierarchical Mixture-of-Experts with Two-Stage Optimization

  Gleb Molodtsov, Alexander Miasnikov, Aleksandr Beznosikov · 12 mai 2026

  Sparse Mixture-of-Experts (MoE) models scale capacity by routing each token to a small subset of experts. However, their routers exhibit a fundamental trade-off: strong load balancing can suppress expert specialization, while aggressive diversity often causes routing collapse. We propose Hi-MoE, a g…

• Unsolvability Ceiling in Multi-LLM Routing: An Empirical Study of Evaluation Artifacts

  Saloni Garg, Amit Sagtani · 11 mai 2026

  Efficient routing across multiple LLMs enables cost-quality tradeoffs by directing queries to the cheapest capable model. Prior work attributes routing headroom to an "unsolvability ceiling", queries no model in the pool can solve. We present a large-scale study of multi-tier LLM routing with 206,00…

• SparseRL-Sync: Lossless Weight Synchronization with ~100x Less Communication

  Lucas Hu, Ranchi Zhao, Isaac Zhu, Zach Zhang, Hscos Zhang, Hugh Yin, Jason Zhao · 11 mai 2026

  In large-scale reinforcement learning (RL) systems with decoupled Trainer-Rollout execution, the Trainer must regularly synchronize policy weights to the Rollout side to limit policy staleness. When inter-node bandwidth is abundant, such synchronization is usually only a small fraction of end-to-end…

• Is Escalation Worth It? A Decision-Theoretic Characterization of LLM Cascades

  Dylan Bouchard · 8 mai 2026

  Model cascades, in which a cheap LLM defers to an expensive one on low-confidence queries, are widely used to navigate the cost-quality tradeoff at deployment. Existing approaches largely treat the deferral threshold as an empirical hyperparameter, with limited guidance on the geometry of the result…

• OpenG2G: A Simulation Platform for AI Datacenter-Grid Runtime Coordination

  Jae-Won Chung, Zhirui Liang, Yanyong Mao, Jiasi Chen, Mosharaf Chowdhury, Vladimir Dvorkin · 8 mai 2026

  AI's growing compute demand and new datacenter buildouts present major capacity and reliability challenges for the electricity grid, leading to multi-year interconnection delays for new datacenters and bottlenecking AI growth. To ease this strain, datacenters increasingly offer rapid power flexibili…

• Resilient AI Supercomputer Networking using MRC and SRv6

  Joao Araujo, Alex Chow, Mark Handley, Ryder Lewis, Christoph Paasch, Jitendra Padhye, Michael Papamichael, Greg Steinbrecher, Amin Tootoonchian, Lihua Yuan, S. Anantharamu, Abhishek Dosi, Mohit Garg, Mahdieh Ghazi, Torsten Hoefler, Deepal Jayasinghe, Jithin Jose, Abdul Kabbani, Guohan Lu, Yang Wang, K. Doddapaneni, Murali Garimella, Vipin Jain, Yanfang Le, H. Nagulapalli, S. Narayanan, Rong Pan, Rathina Sabesan, Raghava Sivaramu, Rip Sohan, Eric Davis, Dragos Dumitrescu, Mohan Kalkunte, Bhaswar Mitra, Guglielmo Morandin, Adrian Popa, Costin Raiciu, Eric Spada, John Spillane, Niranjan Vaidya, Aviv Barnea, Idan Burstein, Elazar Cohen, Yamin Friedman, Noam Katz, Masoud Moshref, Yuval Shpigelman, Shahaf Shuler, Shy Shyman, Sayantan Sur · 7 mai 2026

  Tail latency dominates the performance of synchronous pretraining jobs when running at very large scales. We describe a three-pronged approach: (1) a new RDMA-based transport protocol, MRC, sprays across many paths and actively load-balances between them, eliminating the issue of flow collisions (2)…

• Beyond State Machines: Executing Network Procedures with Agentic Tool-Calling Sequences

  Purna Sai Garigipati, Onur Ayan, Kishor Chandra Joshi, Xueli An · 6 mai 2026

  Agentic AI will be an essential enabling technology for designing future mobile communication systems, which could provide flexible and customized services, automate complex network operations, and drive autonomous decision-making across the network. This work studies how Large Language Model (LLM)-…

• Network Digital Untwinning: Towards Backward Optimization of Digital Twins

  Zifan Zhang, Dianwei Chen, Anjun Gao, Manhua Wang, Mingzhe Chen, Minghong Fang, Xianfeng Yang, Yuchen Liu · 4 mai 2026

  Network digital twins (NDTs) are transforming network management by offering precise virtual replicas of physical network systems. However, their reliance on diverse and sensitive data introduces significant challenges related to data management, regulatory compliance, and user privacy. In scenarios…

• Transformer-Empowered Actor-Critic Reinforcement Learning for Sequence-Aware Service Function Chain Partitioning

  Cyril Shih-Huan Hsu, Anestis Dalgkitsis, Chrysa Papagianni, Paola Grosso · 1 mai 2026

  In the forthcoming era of 6G networks, characterized by unprecedented data rates, ultra-low latency, and ubiquitous connectivity, effective management of Virtualized Network Functions (VNFs) is essential. VNFs are software-based counterparts of traditional hardware devices that facilitate flexible a…

• TIO-SHACL: Comprehensive SHACL validation for TMF Intent Ontologies

  Jean Martins, Leonid Mokrushin, Marin Orlic · 1 mai 2026

  Intent-based networking promises to revolutionize telecommunications network management by enabling operators to specify high-level goals rather than low-level configurations. The TM Forum Intent Ontology (tio) provides a standardized vocabulary for expressing network intents, yet lacks formal valid…

• NeuralEmu: in situ Measurement-Driven, ML-based, High-Fidelity 5G Network Emulation

  Haoran Wan, Yaxiong Xie, Kyle Jamieson · 30 avril 2026

  Current and future applications demand ultra-low latency and consistent throughput, yet frequently traverse 5G cellular networks, so cope with volatile packet dynamics, as 5G base station schedulers dynamically react to user workloads and wireless channel conditions. The task of evaluating network a…

• Multi-Plane HyperX: A Low-Latency and Cost-Effective Network for Large-Scale AI and HPC Systems

  Ziyu Wang, Fei Lei, Dezun Dong · 28 avril 2026

  Multi-plane architectures have become increasingly prevalent in the Fat-Tree networks of AI data centers. By leveraging multiple ports on a single network interface card (NIC) or multiple NICs within a scale-up domain, each port or NIC is allocated to an independent network plane, thereby provisioni…

• Preserving Long-Tailed Expert Information in Mixture-of-Experts Tuning

  Haoze He, Xingyuan Ding, Xuan Jiang, Xinkai Zou, Alex Cheng, Yibo Zhao, Juncheng Billy Li, Heather Miller · 28 avril 2026

  Despite MoE models leading many benchmarks, supervised fine-tuning (SFT) for the MoE architectures remains difficult because its router layers are fragile. Methods such as DenseMixer and ESFT mitigate router collapse with dense mixing or auxiliary load-balancing losses, but these introduce noisy gra…

• SDNGuardStack: An Explainable Ensemble Learning Framework for High-Accuracy Intrusion Detection in Software-Defined Networks

  Ashikuzzaman, Md. Saifuzzaman Abhi, Mahabubur Rahman, Md. Manjur Ahmed, Md. Mehedi Hasan, Md. Ahsan Arif · 24 avril 2026

  Software-Defined Networking (SDN) is another technology that has been developing in the last few years as a relevant technique to improve network programmability and administration. Nonetheless, its centralized design presents a major security issue, which requires effective intrusion detection syst…

• Forecasting Individual NetFlows using a Predictive Masked Graph Autoencoder

  Georgios Anyfantis, Pere Barlet-Ros · 23 avril 2026

  In this paper, we propose a proof-of-concept Graph Neural Network model that can successfully predict network flow-level traffic (NetFlow) by accurately modelling the graph structure and the connection features. We use sliding-windows to split the network traffic in equal-sized heterogeneous bidirec…

• Route to Rome Attack: Directing LLM Routers to Expensive Models via Adversarial Suffix Optimization

  Haochun Tang, Yuliang Yan, Jiahua Lu, Huaxiao Liu, Enyan Dai · 17 avril 2026

  Cost-aware routing dynamically dispatches user queries to models of varying capability to balance performance and inference cost. However, the routing strategy introduces a new security concern that adversaries may manipulate the router to consistently select expensive high-capability models. Existi…

• MLDAS: Machine Learning Dynamic Algorithm Selection for Software-Defined Networking Security

  Pablo Benlloch, Oscar Romero, Antonio Leon, Jaime Lloret · 17 avril 2026

  Network security is a critical concern in the digital landscape of today, with users demanding secure browsing experiences and protection of their personal data. This study explores the dynamic integration of Machine Learning (ML) algorithms with Software-Defined Networking (SDN) controllers to enha…

• Towards Trustworthy 6G Network Digital Twins: A Framework for Validating Counterfactual What-If Analysis in Edge Computing Resources

  Julian Jimenez Agudelo, Paola Soto, Ayat Zaki-Hindi, Jean-S\'ebastien Sottet, S\'ebastien Faye, Nina Slamnik-Krije\v{s}torac, Johann Marquez-Barja, Miguel Camelo Botero · 17 avril 2026

  Network Digital Twins (NDTs) enable safe what-if analysis for 6G cloud-edge infrastructures, but adoption is often limited by fragmented workflows from telemetry to validation. We present a data-driven NDT framework that extends 6G-TWIN with a scalable pipeline for cloud-edge telemetry aggregation a…

• A Comprehensive Survey on Network Traffic Synthesis: From Statistical Models to Deep Learning

  Nirhoshan Sivaroopan, Kaushitha Silva, Chamara Madarasingha, Thilini Dahanayaka, Guillaume Jourjon, Anura Jayasumana, Kanchana Thilakarathna · 16 avril 2026

  Synthetic network traffic generation has emerged as a promising alternative for various data-driven applications in the networking domain. It enables the creation of synthetic data that preserves real-world characteristics while addressing key challenges such as data scarcity, privacy concerns, and …