# BPDM-GCN: Backup Path Design Method Based on Graph Convolutional Neural Network
## 5 Experiment and Result Analysis  
### Experimental Environment and Parameter Configuration  
The experimental hardware setup features high-performance components, including an Intel Core i7-13700H processor clocked at 5.0 GHz, an NVIDIA RTX 4060 graphics card, 16 GB of RAM, and a 1 TB solid-state drive. These specifications ensure robust performance and stable operation throughout the experiment. The operating system used is Ubuntu version 18.0, paired with Python environment version 3.6.9. The BPDM-GCN backup path algorithm computes backup paths utilizing the SDN architecture as its framework. The DDPG algorithm, which has been enhanced through the integration of GCN in the application plane, is trained iteratively in accordance with the link status information, thereby generating a comprehensive link weight. In the control plane, the V 4.34 Ryu controller is employed to collect link status information in the data plane in real time and transmit it to the enhanced DDPG agent. The primary and secondary path information is calculated according to the comprehensive link weight and then sent to the OpenFlow switch via the flow table item. The data plane employs OpenFlow switches and links in the V 2.3.0 Mininet simulation software to simulate the communication network topology environment of the wide-area measurement system integrated with SDN. Although the IEEE benchmark provides a standard basis for network feature evaluation, Mininet's ability to customize network topology, configure network parameters in detail, simulate network protocols and applications, and perform network testing can more accurately reflect the complexity and dynamics of different network environments, providing more comprehensive simulation support for network research and development. The V 1.3 OpenFlow protocol is employed between the control plane and the data plane to guarantee the secure transmission of information between the Ryu controller and the OpenFlow switch.  
To comprehensively validate the performance of the BPDM-GCN backup path algorithm, this study conducts performance testing using six standard IEEE benchmark power system communication network topologies [22]. This paper defines the bandwidth of each link in the communication network topology as 1 Gbps and sets a transmission delay of 1 ms/200 km for the link. Iperf is employed for the simulation and generation of phasor data flow within the network topology of the wide-area measurement system. In order to facilitate the calculation of the path weight of the communication network topology in the IEEE benchmark power system, this paper follows the simplified method in reference [23] and sets the communication network topology to be laid 1:1 with the power cables in the IEEE benchmark power system. According to the PDC and PMU installation algorithms in references [24,25], the positions of switches connected to PDC and PMU in the network topology are determined. The communication network topology data of six standard IEEE benchmark power systems are shown in Table 2.  
\begin{table}
\begin{tabular}{c c c c} \hline
**IEEE Benchmark Power System Name** & **Number of Nodes** & **PDC Location** & **PMU Location** \\ \hline
14-bus & 14 & Bus 11 & 2,6,7,9 \\ \hline
24-bus & 24 & Bus 11 & 2,3,8,10,16,21,23 \\ \hline \end{tabular}
\end{table}
Table 2: IEEE benchmark test power system communication network topology.