Network Traffic and Congestion Control

Últimos artículos

• Tuning Collective Patterns to Alleviate Congestion in Shared AI Clusters

  Eashan Gupta, Yongzhou Chen, Apoorve Mohan, Pavlos Maniotis, Abdullah Kayi, Radhika Mittal · 7 de septiembre de 2026

  Distributed AI training involves recurring rounds of data exchange between multiple pairs of GPU nodes. Slowdown in even one flow due to congestion can cause the entire communication round to slowdown. Current approaches for evading congestion in AI clusters assume global control over the entire wor…

• Toward Non-Expert Customized Congestion Control: Large Language Model-Assisted CCA Code Generation with eBPF Deployment

  Mingrui Zhang, Hamid Bagheri, Lisong Xu · 23 de junio de 2026

  General-purpose congestion control algorithms (CCAs) are designed to achieve general congestion control goals, but they may not meet the specific requirements of certain users. Customized CCAs can meet certain users' specific requirements; however, non-expert users often lack the expertise to implem…

• Efficient Traffic State Prediction With Dynamic Joint Spatio-Temporal Relation Inference

  Zhifeng Hao, Kai Hu, Juncai Zhang, Zhidan Zhao, Zhengming Chen · 23 de junio de 2026

  Traffic prediction is difficult due to the complex interplay of temporal evolution, spatial interactions, and delayed spatio-temporal propagation over road networks. Existing methods either model spatial and temporal dependencies separately or employ unified spatio-temporal structures, but they ofte…

• RouteJudge: An Open Platform for Reproducible and Preference-Aware LLM Routing

  Guannan Lai, Haoran Hu, Han-Jia Ye · 18 de junio de 2026

  We present RouteJudge, an online pairwise preference evaluation framework for LLM routing systems, with a public platform available at https://routejudge.cn. Different from model-level response evaluation, RouteJudge focuses on router-level decision quality. For each user query, multiple routing str…

• Worst-Case Discovery and Runtime Protection for RL-Based Network Controllers

  Hongyu H\`e, Minhao Jin, Maria Apostolaki · 7 de mayo de 2026

  RL-based controllers achieve strong average-case performance in networking tasks such as congestion control and adaptive bitrate streaming. Yet their performance can degrade severely under network conditions where strong performance is still achievable. Identifying such conditions and quantifying th…

• Reinforcement Learning for Intensity Control: An Application to Choice-Based Network Revenue Management

  Huiling Meng, Ningyuan Chen, Xuefeng Gao · 14 de abril de 2026

  Intensity control is a class of continuous-time dynamic optimization problems with many important applications in Operations Research including queueing and revenue management. In this study, we propose a practical continuous-time reinforcement learning framework for intensity control using choice-b…

• TRACE: Traceroute-based Internet Route change Analysis with Ensemble Learning

  Raul Suzuki, Rodrigo Moreira, Pedro Henrique A. Damaso de Melo, Larissa F. Rodrigues Moreira, Fl\'avio de Oliveira Silva · 6 de abril de 2026

  Detecting Internet routing instability is a critical yet challenging task, particularly when relying solely on endpoint active measurements. This study introduces TRACE, a MachineLearning (ML)pipeline designed to identify route changes using only traceroute latency data, thereby ensuring independenc…

• Deep Adaptive Rate Allocation in Volatile Heterogeneous Wireless Networks

  Gregorio Maglione, Veselin Rakocevic, Markus Amend, Touraj Soleymani · 24 de marzo de 2026

  Modern multi-access 5G+ networks provide mobile terminals with additional capacity, improving network stability and performance. However, in highly mobile environments such as vehicular networks, supporting multi-access connectivity remains challenging. The rapid fluctuations of wireless link qualit…

• Is Retraining-Free Enough? The Necessity of Router Calibration for Efficient MoE Compression

  Sieun Hyeon, Jaeyoung Do · 4 de marzo de 2026

  Mixture-of-Experts (MoE) models scale capacity efficiently, but their massive parameter footprint creates a deployment-time memory bottleneck. We organize retraining-free MoE compression into three paradigms - Expert Pruning, Expert Editing, and Expert Merging - and show that persistent post-compres…

• Toward Non-Expert Customized Congestion Control

  Mingrui Zhang, Hamid Bagheri, Lisong Xu · 2 de febrero de 2026

  General-purpose congestion control algorithms (CCAs) are designed to achieve general congestion control goals, but they may not meet the specific requirements of certain users. Customized CCAs can meet certain users' specific requirements; however, non-expert users often lack the expertise to implem…

• Adaptive KDE for Real-Time Thresholding: Prioritized Queues for Financial Crime Investigation

  Danny Butvinik, Nana Boateng, Achi Hackmon · 22 de enero de 2026

  We study the problem of converting a stream of risk scores into one or more review queues under explicit intake constraints[cite: 6]. Instead of top-$K$ or manually tuned cutoffs, we fit an online adaptive kernel density to the score stream, transform the density into a tail-mass curve to meet capac…

• A Deep Reinforcement Learning-Based TCP Congestion Control Algorithm: Design, Simulation, and Evaluation

  Efe A\u{g}lamazlar, Emirhan Eken, Harun Batur Ge\c{c}ici · 21 de enero de 2026

  This paper introduces a Deep Reinforcement Learning (DRL) based TCP congestion-control algorithm that uses a Deep Q-Network (DQN) to adapt the congestion window (cWnd) dynamically based on observed network state. The proposed approach utilizes DQNs to optimize the congestion window by observing key …

• Lens: A Knowledge-Guided Foundation Model for Network Traffic

  Xiaochang Li, Chen Qian, Qineng Wang, Jiangtao Kong, Yuchen Wang, Ziyu Yao, Bo Ji, Long Cheng, Gang Zhou, Huajie Shao · 15 de enero de 2026

  Network traffic refers to the amount of data being sent and received over the Internet or any system that connects computers. Analyzing network traffic is vital for security and management, yet remains challenging due to the heterogeneity of plain-text packet headers and encrypted payloads. To captu…

• BALLAST: Bandit-Assisted Learning for Latency-Aware Stable Timeouts in Raft

  Qizhi Wang · 25 de diciembre de 2025

  Randomized election timeouts are a simple and effective liveness heuristic for Raft, but they become brittle under long-tail latency, jitter, and partition recovery, where repeated split votes can inflate unavailability. This paper presents BALLAST, a lightweight online adaptation mechanism that rep…

• A Multi-Agent, Policy-Gradient approach to Network Routing

  Nigel Tao, Jonathan Baxter, Lex Weaver · 4 de diciembre de 2025

  Network routing is a distributed decision problem which naturally admits numerical performance measures, such as the average time for a packet to travel from source to destination. OLPOMDP, a policy-gradient reinforcement learning algorithm, was successfully applied to simulated network routing unde…

• Q-Net: Queue Length Estimation via Kalman-based Neural Networks

  Ting Gao, Elvin Isufi, Winnie Daamen, Erik-Sander Smits, Serge Hoogendoorn · 1 de diciembre de 2025

  Estimating queue lengths at signalized intersections is a long-standing challenge in traffic management. Partial observability of vehicle flows complicates this task despite the availability of two privacy preserving data sources: (i) aggregated vehicle counts from loop detectors near stop lines, an…

• TURBOTEST: Learning When Less is Enough through Early Termination of Internet Speed Tests

  Haarika Manda, Manshi Sagar, Yogesh, Kartikay Singh, Cindy Zhao, Tarun Mangla, Phillipa Gill, Elizabeth Belding, Arpit Gupta · 27 de octubre de 2025