RouteNet-Fermi: Network Modeling With GNN (Analysis And Re-implementation)

TL;DR

This paper extends RouteNet-Fermi, a GNN-based network performance prediction model, by integrating RNN variants like LSTM and RNN cells, enhancing its ability to model complex network behaviors.

Contribution

It introduces recurrent neural network variants into the GNN architecture for network modeling, providing a flexible framework for future research.

Findings

Improved network performance prediction with RNN variants.

Enhanced understanding of recurrent architectures in GNN-based network models.

Framework supports experimentation with different RNN cell types.

Abstract

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.