## 5 Performance Evaluation
### Performance Comparison  
Figure 3 shows the total and completed task processing time of TaVs, and the total task completion time is the total task processing time minus the task processing time that cannot be completed. The proposed algorithm has the lowest value. The task processing time of all algorithms increases linearly except the completed time of the LESE algorithm. When the number of TaVs is fewer than 20, with the increase of tasks, the LESE algorithm's task completion time gradually increases. Specifically, when the number of tasks is fewer than 15, there is little difference between the completed and total task processing time. In this case, ES can assist the TaVs in completing their tasks within a specified delay. When the number of tasks increases, the number of tasks that cannot be completed increases, and the computation resources of ES cannot provide enough services. Thus, the gap between the two times becomes larger and larger. When the number of tasks equals 40, the completed task processing time is slightly larger than that of LEA. Since TaV has limited resources, the two curves of the LEA algorithm are at the outermost. Specifically, LEA has the largest total task processing time and the smallest completed task processing time. Compared with LEA, the performance of LSVE has improved a lot, which means that SeVs help a lot. Furthermore, with the help of ES, the total processing time of the LSVLESE algorithm is smaller than LSVE, and the completed processing time of the two algorithms are the opposite. The two times of BR-CMCRA coincide, which means the tasks can be totally completed in our proposed algorithm. The maximum task execution time is the maximum task execution time of those tasks that can be completed within the limited latency. As shown in Figure 4, the maximum time of our proposed BR-CMCRA algorithm is the minimal one. Except for the LESE algorithm, the curves of other algorithms are the same and do not cross each other. The LESE algorithm crosses with the LEA algorithm and the LSVE algorithm. As the tasks executed in ES increase, the computing resources of ES become insufficient, and the performance of the LESE algorithm will decline. \begin{table}
\begin{tabular}{l c c c c c c c c} \hline \hline
**Number of Vehicles** & **5** & **10** & **15** & **20** & **25** & **30** & **35** & **40** \\ \hline BR-CMCRA & 0.0129 & 0.0127 & 0.0112 & 0.0099 & 0.0089 & 0.0080 & 0.0073 & 0.0067 \\ LESE [13] & 0.0262 & 0.0312 & 0.0309 & 0.0276 & 0.0240 & 0.0201 & 0.0172 & 0.0155 \\ LSVLESE [19] & 0.2063 & 0.1875 & 0.1955 & 0.1038 & 0.2066 & 0.2081 & 0.2164 & 0.2131 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The convergence of computation resource allocation. As shown in Figure 5, the average throughput of each algorithm is given. As the LEA algorithm executes tasks locally, its throughput is constant zero. We disregard it in the figure. Because the number of TaVs and the number of SeVs of the LSVE algorithm increases 1:1, the throughput of each vehicle is relatively stable, which is from V2V links. Except for V2V links, LSVLESE also uses the V2I links; the throughput of the LSVLESE algorithm is larger than that of the LSVE algorithm. The BR-CMCRA and LESE algorithms' trends are similar, which can reflect the influence of ES to a degree. In addition, the difference in values between the two algorithms can reflect the influence of SeVs to a degree. Figure 4: The maximum time of all tasks versus the number of TaVs. Figure 3: The total/completed task processing time versus the number of TaVs. As shown in Figure 6, the ratio of TaVs executing tasks in different modes are given, which can reflect the execution tendency of TaVs' tasks under these algorithms. The LSVE algorithm and the LEA algorithm have only one execution mode.