## 2 Related Work  
Network slicing has been recently considered in a number of research works, particularly for vehicular networks. In [15]; the authors gave priority to the safety data. A queuing method was used to categorize the traffic data based on the priority. Fur thermore, they applied deep neural network methods for resource allocation in VANET. However, the structure of data set in terms of features, size, and output classes was not clearly explained. Based on the results, Long Short-Term Memory (LSTM) performs better than both Convolutional Neural Network (CNN) and Deep Neural Network (DNN). Unfortunately, the time-based results, such as resource allocation time and detection time, have not been provided. In addition, no further information was provided on the amount of bandwidth dedicated to each of the priority queues and how this amount of bandwidth can be assigned to the vehicles. Network slicing requirements for Vehicle-to-everything (V2X) communications are investigated in [16]. The authors believe that, apart from infrastructure and management layers, a network slicing V2X architecture also needs to be designed while using business and service layers. These two layers must be considered to determine which services can be provided and which use cases can be supported for each network slice. In [17], the k-means++ technique is proposed for clustering the services based on the Service Level Agreement (SLA). After clustering the services in three categories of traffic safety, traffic efficiency, and information services, then multiple services are assigned to each slice. In addition, Shared Proportional Fairness Scheme (SPFS) is proposed to schedule network slices based on fairness in resources utilization. Based on the results, wireless resource utilization rate could be improved using the linear programming barrier method and slice scheduling approach. In [18], the Euclidean distance method is used to find similar vehicles in terms of weak Quality of Experience (QoE). Subsequently, the similar vehicles are gathered in the same cluster. A vehicle that has better QoE and stronger communication links is pronounced as the leader in the slice. Moreover, the Lyapunov optimization technique was applied to provide video frames by the leaders for the other vehicles in the slices. Based on the results, in the slices with high vehicle density, the quality of the selected video is low and the QoE in these slices is reduced. The Lyapunov optimization algorithm selects the low quality videos to guarantee the stability of streaming video. Because the radio resources could not support the high number of vehicles in the slice, the low quality videos are selected by the Lyapunov algorithm. Although it is a wise technique for selecting video data, it could also improve the communication link capacity by dynamically changing the size of the slices and making new slices with a lower number of QoE vehicles. In [19], the authors proposed an intelligent cloud based architecture for network slicing in vehicular networks. In this architecture, a deep reinforcement learning method is proposed to be applied in the control layer. Collecting, storing, and analyzing the huge data are to be done at the control layer. However, these tasks require powerful computing and storage resources; otherwise, the performance of the intelligent method will be negatively affected. Moreover, using cloud for these tasks is not an optimal solution; instead, the fog devices could be a better choice for these challenges. In [20], the author proposed a stochastic method for network slicing scheduling and resource allocation. [PERSON] and Lyapunov methods were used for the nonlinear stochastic optimization problem of resource utilization in vehicular networks. The proposed method optimized the value of transmission power and the value of transmission rate in network slices.