# Stochastic Modeling for Intelligent Software-Defined Vehicular Networks: A Survey
## 5 Performance Evaluation of iSDVN
### Integration of SDN with Other Technologies  
The integration of SDN with other emerging technologies is an important trend in the development of vehicular networks. The integration of SDN with other technologies can enable new services, enhance network performance, and improve the overall efficiency of vehicular networks. One technology that can be integrated with SDN is edge computing. Essentially, edge computing is meant to bring computation and data storage closer to the data generation edge of a network. By integrating edge computing with SDN, it is possible to reduce the latency and improve the response time of vehicular applications. For example, the combination of edge computing and SDN can enable intelligent decision making by analyzing data in real time at the edge of the network. Another technology that can be integrated with SDN is artificial intelligence (AI). AI can be used to enhance the intelligence of vehicular networks by analyzing and learning from data collected by the network. By integrating AI with SDN, it is possible to develop more efficient and intelligent routing algorithms and to optimize network resources for better performance. The integration of 5G with SDN is also a promising trend. Offering high-speed connectivity and low latency, 5G is the next generation of wireless communication technology. By integrating 5G with SDN, it is possible to provide reliable and efficient communication for vehicular networks. SDN can be used to optimize the use of 5G resources and to enable more efficient traffic management and routing. Finally, integrating Blockchain technology with SDN can provide additional security and privacy for vehicular networks. Blockchain can be used to create a tamper-proof ledger of all the transactions that occur in the network, which can help prevent malicious attacks and ensure users' privacy [139]. Integrating SDN with other emerging technologies, such as edge computing, AI, 5G, and Blockchain, can enhance vehicular networks' intelligence, performance, and security. This integration is an important trend that is driving the development of next-generation vehicular networks. _SDN in VANETs_: Stochastic network optimization and SDVNs are presented in this section to improve packet forwarding. The source and destination nodes of VANETs operate on a queueing model. VANETs are quickly becoming SDN networks with the help of new technologies. The IEEE 802.11p/1609 vehicular communication protocol provides efficient data transmission to VANETs [140]. The integration of this technology allows us to monitor vehicles, which improves traffic management and makes transportation more efficient. Various studies have been conducted on VANETs in terms of their efficiency in disseminating data. As SDN changes, the VANET topology can be adjusted. A VANET's control plane is decoupled from its assimilation and network management support. As part of theseservices, network infrastructure virtualization is proposed in [41]. In [42], an architecture for a hierarchical SDVN is proposed. As a result of packet loss and poor connectivity, a communication protocol was developed to address it through the controller [63]. In [37], the authors abstracted heterogeneous wireless devices such as vehicles and RSUs to achieve rapid network innovation. The benefit of logically centralized control planes is that they provide better configuration capabilities by improving the quality of service. The location of vehicles, traffic flows, and the cooperative data scheduling of vehicles have been studied [3]. V2I communication is enabled by RSUs picking up data from source and destination vehicles. SDN-enabled vehicular networks are managed and resource-utilized efficiently by their controllers. Wireless communication technologies are often integrated with SDN paradigms to support vehicular communications [49]. The SDVN routing protocol takes performance metrics into account, resulting in stable routes and the shortest communication delays [8; 58].