# Stochastic Modeling for Intelligent Software-Defined Vehicular Networks: A Survey
## 1 Introduction
Due to the fact that the SDN controller is often the point of failure, dependability is one of the significant drawbacks of SDN. Moreover, SDN has difficulties integrating with traditional networks, which cannot utilize OpenFlow; the centralized controller cannot independently manage all traffic, and there are only a few protocols for communication between the controller and applications [13]. The SDVN is a variant of the software-defined wireless network (SDWN) among wireless networks. The SDVN architecture makes vehicular networks adaptable and versatile by integrating SDN into VANETs [11]. The network perception provided by SDN controllers enables numerous benefits, including adaptive node transmission power reservation, improved routing, and flexible radio interface placement. Due to the collection of network statistics, the SDVN improves networking functions, such as routing and load balancing, and enables global optimization in VANETs. It also promotes network innovation by enabling VANET technologies to be evaluated and implemented at a lower cost. As a result of the extensive mobility of the network nodes, as well as the changeable topology of the network, the SDVN encounters some difficulties, including security gaps and complex network operations, such as routing and transmission management [13]. The rapid advancement of V2V and V2I communication technologies has made intelligent software-defined vehicular networks (iSDVNs) increasingly important. Intelligent Transportation Systems (ITSs), such as traffic management, collision avoidance, and autonomous driving, cannot function properly without efficient and reliable vehicle-to-vehicle and vehicle-to-infrastructure communications, and vice versa [15]. A mathematical approach to analyzing and predicting the behavior of systems with random variables, stochastic modeling, has emerged as a promising tool for designing and assessing intelligent SDVNs [14]. This survey aims to present the state-of-the-art stochastic modeling techniques and their applications in iSDVNs as some implications and possible research in the near future. This survey also highlights the significant challenges of VANETs, including limitations on data dissemination, connectivity, load buffering, network congestion, and multi-hop routing, and how queueing networks, intelligent SDVNs, and software-defined heterogeneous vehicular networks (SDHVNs) can be used to address these challenges. A fundamental change in network management has been made with SDN, which offers flexibility, programmability, and centralized control. However, it is challenging for network administrators to understand and efficiently manage the network state because SDN architectures are dynamic and distributed. On the other hand, digital twin technology has shown great promise in a number of domains, enabling the virtual representation of physical entities, systems, or processes. We aim to enhance network management by providing actionable insights and real-time intelligence by integrating digital twin technology and SDN. Digital twins are virtual representations of network infrastructure, applications, and services interconnected through SDN. The virtual model comprises network components like switches, routers, controllers, and individual packets and flows. The digital twin is created by collecting and integrating real-time data from the physical network to create a comprehensive and current view of the entire SDN ecosystem. By analyzing historical trends, administrators can gain insight into the current state of the network, predict potential issues, and determine key performance bottlenecks, as illustrated in Figure 1.