# Performance Evaluation of Graph Neural Network-Based RouteNet Model with Attention Mechanism
## 3 Methodology  
The block diagram illustrating RouteNet, which is a Graph Neural Network (GNN)-based model, is depicted in Figure 3. RouteNet is a network performance prediction system that takes a given network topology, source-destination routing scheme, and traffic matrix as input, and produces performance metrics for each end-to-end path in the routing scheme [2].  
The model processes various inputs for each sample in the dataset to perform its delay prediction [7]. These inputs include the following:  
* Topology: The input topology represents the network structure using a directed graph, where nodes represent network devices (such as routers or switches) and edges represent physical or logical links connecting these devices. Moreover, topology includes properties such as nodes, links, queue sizes, and link capacity. It provides a detailed description of the physical objects in the network and their interconnections, enabling the model to understand the network's layout and properties.
* Traffic Matrix: The traffic matrix is a matrix representation where the rows and columns correspond to network devices, and the elements indicate the bandwidth or volume of traffic between device pairs. This input provides information about the flow-level and aggregate characteristics of network traffic, including metrics such as average bandwidth, packet generation rates, and average packet size. Analyzing the traffic matrix allows the model to simulate and analyze the traffic patterns and demands within the network, facilitating resource allocation and capacity planning.
* Routing: The routing input consists of a scheme that specifies the paths connecting source-destination pairs in the network. It defines the routes that packets take when traveling from one node to another. By analyzing the routing scheme, the model can evaluate the efficiency and effectiveness of data routing, allowing for optimizations and informed decision making regarding network traffic distribution.  
As the model relies on topology, routing, and traffic information as inputs, obtaining these data from the networking system is crucial. Identifying the topology of large-scale systems, especially those with dynamic topologies, necessitates employing a variety of sophisticated tools and technologies. Automated discovery tools such as SNMP (Simple Network Management Protocol) and LLDP (Link Layer Discovery Protocol) are pivotal in facilitating real-time and continuous topology discovery by dynamically detecting network devices and tracking topology changes. These technologies simplify the topology discovery process in dynamic environments by allowing for automated polling and retrieval of  
Figure 3: Block diagram of GNN-based model.  
network information [22]. Integrating these discovery tools with Network Management Systems (NMS) further enhances the capability to efficiently manage large-scale and dynamic topologies by providing a centralized platform for network topology visualization, change tracking, and alert management [23].  
In our research, publicly available datasets generated from a customized packet-level simulator based on OMNeT++ were utilized, which were collected from [24; 25]. These datasets include samples with different input topologies, routing configurations, and traffic patterns. For this research, four specific topology datasets were used: NSFNet Topology, GEANT2 Topology, GBN Topology, and REDIRIS Topology. During the training phase, the NSFNet and GEANT2 topologies were used, the GBN topology was used for validation, and the REDIRIS topology was used for testing purposes.