## 8 Simulation and Results
### Results  
Figure 4 presents the average value of the obtained PDR for the three systems with the parameters provided in Table 4. The PDR was measured at different traffic densities and distances from the RSUs. The proposed microservice-based VANET (system 3) achieved higher PDR at the three considered traffic densities. With the increase in the TD, the PDR of the existing systems (system (1) and system (2)) was reduced compared with the proposed microservice model. This is mainly due to the deployment of microservices and the proposed migration scheme. Moreover, the PDR of the three systems was measured at different vehicle mobilities. Figure 5 presents the average PDR of the three systems at different vehicle velocities with the specifications presented in Table 5. \begin{table} -  
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Table 4: Parameters for PDR measurements (Figure 4). \begin{table} -  
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Table 5: Parameters for PDR measurements (Figure 5). Figure 5: PDR at different vehicle mobility. Figures 6-9 present the average performance of utilizing computing resources of vehicles, fog, and MEC nodes. This measure was considered to evaluate the performance of the proposed microservice and migration schemes. Also, this investigates the effectiveness of introducing the fog layer to VANETs. These figures measured the average resource utilization at different vehicle mobilities with the specifications introduced in Table 6. Each figure represents the results while simulating the systems with tasks from certain application categories. Figure 6 presents the average resource utilization of the four systems simulated using Category (I) tasks. The proposed model achieved lower utilization performance for this category of applications compared to other simulated systems. This is due to the plurality of available resources, e.g., fog and MEC, compared to the workloads required for processing. Since the assigned tasks are all sensor data that require low computing resources, this makes the resources of end devices, i.e., vehicles, enough to handle most of such tasks. However, with the increase in vehicle velocity, the performance of traditional systems degraded, and the performance of the proposed system increased. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Parameter**} & **Traffic Density** & **Number of Vehicles (N)** & **System (1)** & **System (2)** & **System (3)** & **System (4)** \\ \hline \multirow{2}{*}{Value} & \multirow{2}{*}{0.1 veh/m} & \multirow{2}{*}{400} & Traditional & Traditional & System & Proposed \\  & & & VANET & MEC-VANET & developed in [28] & microservice-based VANET \\ \hline \hline \end{tabular}
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Table 6: Simulation parameters for utilization efficiency measurements (Figures 6–9). Figure 4: PDR at different TDs. Figure 8: Average resource utilization efficiency at different vehicle mobility, for Category (III) tasks. Figure 6: Average resource utilization efficiency at different vehicle mobility for Category (I) tasks. Figure 7: Average resource utilization efficiency at different vehicle mobility for Category (II) tasks. Figures 7 and 8 present the utilization efficiency for the four simulated systems when simulated using tasks from Category (II) and Category (III), respectively. With the increase in task complexity, moving from Category (I) to Category (II) and then to Category (III), the demands for higher computing resources and efficient use of such resources increase. The proposed microservice-based VANET achieved an average utilization of computing resources higher than other existing systems, mainly at high vehicle mobility. Also, this performance improvement increases in Figure 9, which provides the results of Category (IV). This is due to the complexity of tasks that need higher computing capabilities, achieved through introducing the fog layer and efficient use of resources, achieved using microservice and offloading approaches. Moreover, the proposed migration scheme increases efficiency in high vehicle mobilities.