2024/12/07 by Shourya Verma, Simran Kadadi, Verma, Shourya +6
Computer Science · #Energy Efficient Wireless Sensor Networks #FOS: Computer and information sciences #Networking and Internet Architecture (cs.NI)
paper · pdf · doi:10.48550/arxiv.2412.05649
openalex publication_date 2024/12/07 · openalex created_date 2024/12/12 · openalex updated_date 2026/07/28
Network performance modeling presents important challenges in modern computer networks due to increasing complexity, scale, and diverse traffic patterns. While traditional approaches like queuing theory and packet-level simulation have served as foundational tools, they face limitations in modeling complex traffic behaviors and scaling to large networks. This project presents an extended implementation of RouteNet-Fermi, a Graph Neural Network (GNN) architecture designed for network performance prediction, with additional recurrent neural network variants. We improve the the original architecture by implementing Long Short-Term Memory (LSTM) cells and Recurrent Neural Network (RNN) cells alongside the existing Gated Recurrent Unit (GRU) cells implementation. This work contributes to the understanding of recurrent neural architectures in GNN-based network modeling and provides a flexible framework for future experimentation with different cell types.