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<p>Network congestion remains a critical challenge in modern communication systems, affecting performance, reliability, and Quality of Service (QoS). Traditional congestion control mechanisms often rely on reactive approaches, which may lead to inefficiencies in dynamic network environments. This paper proposes a Machine Learning (ML)-based predictive framework leveraging Graph Neural Networks (GNNs) to forecast network congestion before it occurs, enabling proactive traffic management. We model the network as a dynamic graph, where nodes represent routers/switches and edges denote communication links. By incorporating spatial and temporal dependencies, our GNN-based approach predicts congestion hotspots with high accuracy. We evaluate our framework on real-world network datasets, demonstrating superior performance compared to traditional methods (e.g., TCP congestion control) and other ML models (e.g., LSTMs, CNNs). Our results show a 15–25% improvement in prediction accuracy, leading to reduced latency and packet loss in simulated and real testbeds.</p>

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Predictive network congestion management for enterprise systems: a graph neural network approach with operational insights

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年11月10日

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  • Balaji Magar,
  • Rohan Kulkarni,
  • Vaibhav Khatavkar,
  • Saurabh Parhad,
  • Hrishikesh Vanjari

摘要

Network congestion remains a critical challenge in modern communication systems, affecting performance, reliability, and Quality of Service (QoS). Traditional congestion control mechanisms often rely on reactive approaches, which may lead to inefficiencies in dynamic network environments. This paper proposes a Machine Learning (ML)-based predictive framework leveraging Graph Neural Networks (GNNs) to forecast network congestion before it occurs, enabling proactive traffic management. We model the network as a dynamic graph, where nodes represent routers/switches and edges denote communication links. By incorporating spatial and temporal dependencies, our GNN-based approach predicts congestion hotspots with high accuracy. We evaluate our framework on real-world network datasets, demonstrating superior performance compared to traditional methods (e.g., TCP congestion control) and other ML models (e.g., LSTMs, CNNs). Our results show a 15–25% improvement in prediction accuracy, leading to reduced latency and packet loss in simulated and real testbeds.