# Performance Evaluation of Graph Neural Network-Based RouteNet Model with Attention Mechanism
## 2 Background and Related Work
### Related Work
The authors compared RouteNet-Erlang to a state-of-the-art Queuing Theory (QT) model and showed that it outperformed QT in all network scenarios. [PERSON] et al. [15] developed a network model called RouteNet that uses Graph Neural Networks (GNNs) to accurately predict Key Performance Indicators (KPIs) such as delay, jitter, and loss in Software-Defined Networks. The authors demonstrated that RouteNet can generalize over various network topologies, routing schemes, and traffic intensity, and can accurately predict KPIs even in unseen scenarios. The paper highlights the limitations of existing analytic models for network modeling and emphasizes the potential of GNNs for building accurate and flexible data-driven models for network optimization. Furthermore, the authors presented use cases where they used RouteNet for efficient routing optimization and network planning. [PERSON] et al. [16] introduced an extension to the RouteNet model, allowing for the modeling of forwarding devices with different characteristics, specifically, variable queue sizes. The model was evaluated through experiments to assess its accuracy in predicting network performance metrics. In 2022, [PERSON] et al. [17] addressed 5G network generalization challenges with QTRoutNet, utilizing robust features from Queuing Theory (QT). By fine-tuning baseline predictions with a modified RouteNet GNN model, they significantly improved generalization, reducing the mean absolute percent error from 10.42 to 1.45. QTRoutNet's success indicates that enhancing network modeling accuracy can be achieved effectively through minor modifications to a robust approximate model without sacrificing generalization. DeepQSC, proposed by [PERSON] et al. [18], addresses the QoS-based service composition problem through an end-to-end supervised learning framework incorporating attention mechanisms. It effectively navigates challenges related to diverse service compositions, intricate topological relationships, and varying subfunction providers. Leveraging attention mechanisms, the model excels in capturing complex relationships among web services, leading to superior QoS-aware composite service formation. [PERSON] et al. [19], introduced STGMAGNet, a pioneering approach for predicting long-term airport delays, incorporating attention mechanisms. Utilizing a Spatial-Temporal Gated Multi-Attention Graph Network, the model proficiently captures intricate correlations among airports and time steps. This attention-centric strategy enhanced precision, addressing challenges such as error propagation and dynamic correlations. [PERSON] et al. [20] conducted an extensive review of Graph Neural Network (GNN) applications in network management, encompassing task offloading, routing optimization, and resource allocation. The paper explored the GNN's role in enhancing delay awareness for tasks like offloading and routing optimization in network management. The survey extensively covered SDN/NFV-enabled networks, emphasizing objectives such as energy efficiency, QoS improvement, resource utilization, cost optimization, and link loading. [PERSON] et al. [21] present a thorough survey on attention-enhanced Graph Neural Networks (GNNs), addressing a gap in systematic exploration. Introducing a novel two-level taxonomy, it categorizes attention-based GNNs into graph recurrent attention networks, graph attention networks, and graph transformers. This study explores the enhancement of GNN functionality through the integration of attention mechanisms.