# Performance Evaluation of Graph Neural Network-Based RouteNet Model with Attention Mechanism
## 2 Background and Related Work
### Related Work  
The paper by [PERSON] et al. [1] addresses the scalability problem of Graph Neural Networks (GNNs) for network control and management. The GNN has shown great potential in accurately predicting network properties, but existing solutions have a limitation in generalizing to larger networks. To overcome this limitation, the authors propose a GNN-based solution that can effectively scale to larger networks with higher link capacities and aggregated traffic on links. The approach has been evaluated and shown to significantly improve the accuracy of network predictions on larger graphs. In 2019 [2], a network model called RouteNet was proposed that uses Graph Neural Networks (GNNs) to accurately predict end-to-end Key Performance Indicators (KPIs) such as delay and jitter in networks. They evaluated RouteNet and showed that it can make performance predictions with similar accuracy to packet-level simulators, even in network scenarios unseen during training. [PERSON] et al. [5] developed a GNN model that accurately estimates delay and jitter in self-driving Software-Defined Networks by understanding the complex relationship between topology, routing, and input traffic. The model generalized arbitrary topologies and routing schemes and has been tested against unseen scenarios, achieving a worst-case R2 of 0.86. The paper also presents the model's potential for network operation, including its effectiveness in delay/jitter routing optimization for specific source/destination pairs and its ability to reason in topologies and routing schemes not seen during training. [PERSON] et al. [7] presented an approach to address the generalization problem of Graph Neural Networks (GNNs) by leveraging networking concepts and analysis to improve the baseline RouteNet model developed by Barcelona Neural Networking Center - Universitat Polit'ecnica de Catalunya (BNN-UPC). The proposed method was able to significantly enhance the accuracy of the model in larger graphs, which is an important step toward using GNNs for modeling and optimizing modern networks. In addition, the paper proposes a closed-loop control context where the resulting GNN model could potentially be applied in different use cases. [PERSON] [8] presented an extension to the RouteNet model, which uses Graph Neural Networks (GNNs) to estimate network performance metrics based on topology, routing configuration, and traffic demands. The model was evaluated on a dataset of network scenarios with varying queue sizes, demonstrating progress to  
Figure 2: Detailed Attention Mechanism. ward building more realistic network models for optimizing and managing real-world computer networks. In 2018, [PERSON] et al. [12] explored the application of neural networks (NNs) for modeling the average end-to-end delay in computer networks based on input traffic matrices. While the study underscores the accuracy and efficiency of well-designed NNs, a notable disadvantage is their data-driven nature, demanding large training sets and significant computational resources during the learning phase. Despite these limitations, NNs prove effective in accurately modeling average end-to-end delay in communication networks, provided proper tuning of hyperparameters, with the primary drawback being their resource-intensive initial training process. In a study conducted in [13], it was demonstrated that RouteNet-Fermi is an accurate and effective network model for predicting delay, jitter, and loss in complex networks. The model was found to have comparable accuracy to computationally expensive packet-level simulators and was able to scale accurately to large networks with a mean relative error of 6.24% on a test dataset. [PERSON] et al. [14] introduced a new technique called RouteNet-Erlang, which utilizes Graph Neural Networks (GNNs) to model computer networks. This approach supports complex traffic models, multi-queue scheduling policies, and routing policies, and can provide accurate estimates in networks not seen during training.