## 2 Methodology
### SDVN Controller Deployment Results  
Mininet simulation software was developed for experiments, and a computational verification environment was established for deterministic annealing clustering algorithms and machine learning. Deep learning Keras was built on a desktop computer with the following hardware and software configurations: an AMD Ryzen 7 1800X Eight-Core Processor with a clock speed of 3.60 GHz as the CPU, a Ubuntu 20.04 operating system, 8 GB of RAM, and an NVIDIA GeForce GTX 1060 6 GB as the GPU, which is shown in Table 1.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Hardware/Software** & **Model/Specification** \\ \hline Operating System & Ubuntu 20.04 operating system \\ \hline Memory & 8 GB \\ \hline Processor & AMD Ryzen 7 1800X Eight-Core Processor 3.60 GHz (AMD, Santa Clara, CA, USA) \\ \hline Graphics Card & NVIDIA GeForce GTX 1060 6 GB (NVIDIA, Santa Clara, CA, USA) \\ \hline Programming Language & Python 3.7 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Experiment configuration.  
As show in Table 1, the experimental simulation case selects the streets of Longhua New District, Shenzhen, and builds a multi-area SDVN composed of 42 RSUs in the Mininet simulation environment. Each RSU is connected by a wired network, and most of them are deployed on the main road and the secondary road, as shown in Figure 4:  
Figure 4 depicts a large-scale SDVN consisting of 46 RSUs. The green dots in the graph represent RSUs. Each RSU is composed of OpenFlow switches and servers, labeled from 0 to 45. The 'location' results of the controller solved by the DAC algorithm are shown in Figure 5.  
Figure 5: Result of SDVN controller deployment.  
As shown in Figure 5, the four controller RSUs obtained by solving are No. 21, No. 34, No. 11, and No. 31 RSUs, respectively. In Figure 5, the four controller RSUs and the surrounding ordinary RSU areas are distinguished by green, yellow, red, and orange, respectively. The four controller RSUs are highlighted and represented by four gray circles of A, B, C, and D, respectively.  
Because it is impossible to collect traffic information for all vehicles in the road network, in order to predict SDVN traffic (mainly to predict the level of network traffic, which can provide a basis for network load balancing research), the traffic information provided by taxis (or ride-hailing) equipped with GPS equipment was used for analysis. Every GPS-equipped taxi (or ride-hailing car) periodically reports traffic information to the surrounding RSU. The sampling period is set as 1 s, that is 1 Hz in the application scenario. Since each RSU is located in different parts of the city, the real-time traffic flow and road conditions are different, which will result in a large difference in the information interaction requirements of different RSUs. Therefore, after obtaining the network traffic collected by taxis (or online car-hailing) in the region, the network traffic level of the SDVN after control plane deployment in the region in the future is predicted; SDVN traffic prediction can provide a basis for judging the network load balancing when all urban road vehicles are connected to the network in the future [31].