# Stochastic Modeling for Intelligent Software-Defined Vehicular Networks: A Survey
## 5 Performance Evaluation of iSDVN
### Integration of SDN with Other Technologies
In the control plane, a 5G-enabled SDVN, packet-scheduling algorithms, and SDN architecture were applied for data forwarding to the data plane. Although the authors examined general issues associated with heterogeneous vehicular systems, they do not have a specific solution. Routing and scheduling requirements for data communication in VANETs take into account a variety of network resources. SDN controllers control packet schedulers for heterogeneous vehicular networks [38]. As described in [54], the authors proposed an integrated hierarchical architecture for the IoV, which can enhance the reliability and scalability of data services. Data trafficking and network management have become more flexible, which makes future ITSs possible. The SDVN framework facilitates data dissemination in an efficient and effective manner. The controller in VANETs controls high mobility and multi-hop paths to achieve high reliability and throughput. A dynamic vehicular connection management approach was developed by the authors to guarantee the quality of service in an SDIoV. The authors of [58] investigated various factors related to link stability to optimize routes for packet delivery. The SDN controllers improve packet delivery and decrease latency by managing traffic flow cooperation and network availability. To maximize network utility, SDVN and dynamic stochastic network optimization are studied. Furthermore, the sharing of information at the mobile edge of a SD 5G VANET is emphasized, as well as the improvement of latency and reliability. The utilization of resources and the quality of service that VANETs provide must be balanced. Due to the different QoS requirements for multi-class services, the SDHVN prioritizes different functions and ensures fairness. Vehicle networks place a high priority on safety-related services. An innovative packet-scheduling approach is proposed for resource management in VANETs [145]. SDHVN's data dissemination utility is enhanced by it, as well as the multi-class QoS performance. According to [PERSON] et al. [37], VANET data dissemination poses the largest problem. This scheme enhanced QoS over the available existing models in studies performed by [PERSON] et al. [45], [PERSON] et al. [51], and [PERSON] et al. [57]. Due to the fact that most SDHVNs are cooperative, a novel multi-hop cooperative data dissemination system with packet scheduling can be envisioned for SDHVNs. SDN in Internet of VehiclesThis section integrates the SDN concept with IoV, demonstrating its excellent benefits [146]. The control plane controls data forwarding and flow-table matching. An SDN controller can, however, make better routing decisions based on a global view of the network topology. Table 3 presents a comparison of related work on SDN-based IoV [147]. Centralized/distributed SDN architectures were compared for most work-related tasks. With SD-IoV, latency control is reduced by using the higher mobility of vehicles in SD-VANET systems and the additional link/disconnection that occurs in SD-VANET systems [44]. _Digital Twin Integrated with SDN_: Digital twin technology integrated with SDN can enhance network management in several ways. Data ingestion in the digital twin allows for the real-time monitoring and visualization of network elements, flows, and traffic patterns. To make informed decisions, administrators can visualize and analyze the current state of the network. The digital twin can predict potential network issues or performance bottlenecks based on historical data and network behavior patterns. As a result, administrators can take proactive measures before problems worsen. With the digital twin, administrators can optimize resource allocation, traffic engineering, and load balancing in real time, thus enhancing the efficiency of their networks. When network faults occur, the digital twin can act as a sandbox for conducting virtual experiments to identify the root cause and test potential solutions before implementing them in real time.