## 4 Key Technologies for MEC-Assisted Vehicular Networks
### Software-Defined Networking
After that, a model is created to understand how reliable the system is and to estimate the maximum possible delay in the worst-case scenario, based on the likelihood of connection failures. To enhance user experience by reducing the perceived latency and the cost of relocation, a mobility-aware service-adaptive deployment strategy is proposed. The maximum range of mobility is then estimated using a probabilistic approach. Subsequently, a user mobility-based service deployment overhead model is established, considering computing and communication delays and migration costs. Finally, to avoid frequent service migration, a long-term migration cost is implemented. The last step involves experimental testing and comparison of the proposed strategy which includes implementing cost and connection failure likelihood. In [71], authors devise a MEC-assisted VN model that takes into account the communication interference that occurs when tasks are offloaded or relocated in order to reduce the computational cost. They utilize SDN in order to control data traffic. In the network, a sub-SDN controller collects data related to specific computing tasks of target vehicles and information about idle vehicles. This includes the location, direction of travel, and CPU frequency of the on-board unit of the car. Information about the computation task for the target vehicle includes specifics like the task volume, workload, and the maximum acceptable latency. The vehicle typically transmits information to the closest RSU on a regular basis. The RSUs keep the gathered data in the MEC server to provide them to the SDN controller. They suggest two algorithms--a Task Migration (TM) algorithm and a Computation Overhead Minimization Offloading (COMO) algorithm--that can decrease computation costs and boost task execution success rates. In [72], a unique Software-Defined Vehicular Network (SDVN) architecture for offloading tasks is introduced. Collecting global data about the car, the task, and the RSU can be achieved by implementing the SDVN control system at the routing layer. This facilitates the implementation of an optimal processing scheme for real-time tasks associated with connected cars. They propose an efficient method for evaluating performance through a software-defined predictive task migration scheme. This approach involves modeling the transition of vehicle task offloading, which includes estimating the final delay and power consumption. The SDVN design is structured into three planes: the traffic layer, data layer, and control layer. Vehicles that are on the road and have varying task requirements make up the traffic layer. The task data movement between RSU is included in the data layer. Via an area-deployed SDN controller, the control layer will put the migration decision into action. When a vehicle is in range of an RSU, tasks are offloaded to the RSU via the wireless link. Specific RSUs, acting as fog nodes with robust computing capabilities, can handle these tasks locally. Alternatively, some RSUs may transfer the workload from the local cloud to the central network for processing. The SDVN controller collects and manages information tables for vehicles, fog nodes, and tasks. These tables include the vehicle data, the fog node data, and the task data [72]. In ref. [73], researchers propose a framework for edge computing that leverages SDN. The aim is to provide a more detailed explanation of the task migration and application relocation rules outlined by the ETSI. In this model, they use cloud-native solutions and the Docker container platform for service relocation. The SDN system predicts when service migration will happen, selects the best target MEC host, manages bandwidth, and ensures a smooth transition [74]. SDN acts as a MEC orchestrator in the suggested architecture [73], coordinating MEC hosts and making network-related decisions. Integrating the Docker container framework brings benefits such as application mobility, stability, scalability, and quick implementation times to the MEC design.