M3Net: A Multi-Metric Mixture of Experts Network Digital Twin with Graph Neural Networks

TL;DR

M3Net is a graph neural network-based digital twin that predicts multiple network performance metrics simultaneously, improving accuracy over existing models for complex 5G/6G network management scenarios.

Contribution

It introduces a multi-metric mixture-of-experts network architecture that enhances the accuracy of network performance predictions across various metrics.

Findings

Reduced flow delay prediction error from 20.06% to 17.39%.

Achieved 66.47% accuracy on jitter prediction.

Achieved 78.7% accuracy on packets dropped prediction.

Abstract

The rise of 5G/6G network technologies promises to enable applications like autonomous vehicles and virtual reality, resulting in a significant increase in connected devices and necessarily complicating network management. Even worse, these applications often have strict, yet heterogeneous, performance requirements across metrics like latency and reliability. Much recent work has thus focused on developing the ability to predict network performance. However, traditional methods for network modeling, like discrete event simulators and emulation, often fail to balance accuracy and scalability. Network Digital Twins (NDTs), augmented by machine learning, present a viable solution by creating virtual replicas of physical networks for real- time simulation and analysis. State-of-the-art models, however, fall short of full-fledged NDTs, as they often focus only on a single performance metric or simulated network data. We introduce M3Net, a Multi-Metric Mixture-of-experts (MoE) NDT that uses a graph neural network architecture to estimate multiple performance metrics from an expanded set of network state data in a range of scenarios. We show that M3Net significantly enhances the accuracy of flow delay predictions by reducing the MAPE (Mean Absolute Percentage Error) from 20.06% to 17.39%, while also achieving 66.47% and 78.7% accuracy on jitter and packets dropped for each flow