## 5 Methodology
### Max-Min Fairness Oriented Offloading Approach
The maximum performance value first (maxPFV) scheme in [31]: in this scheme, the distance, relative velocity, link reliability and available computing resources are considered as fuzzy logic inputs to obtain the performance value (PFV) of each service vehicle, and UV selects the SVs sequentially according to their PFV values in a descending manner. 2. The maximum computing resources first scheme (maxCom): UV selects SVs sequentially according to their volumes of computing resources in a descending manner. 3. The minimum price first scheme (minPrice): UV selects SVs sequentially according to their required unit prices in an ascending manner. 4. The random offloading scheme (Random): UV randomly selects SVs for task offloading. The simulation area is set as a 2-km straight road with three lanes, and vehicles run in the same direction. The specific parameter settings are shown in Table 3.

### Performance Comparisons under Different Sizes of Tasks

The number of vehicles on the road is set as 50, a UV generates a computation task to be offloaded to SVs. The size of the task is randomly generated between [5; 30] MB and a maximum tolerable delay is determined between [3; 18] time slots. The change of task completion delay with the size of task data increases is shown in Figure 2(a). We can see that as the size of the task increases, the task completion delay also increases. Moreover, the proposed Algorithm 2 achieves the shortest completion delay among all comparison algorithms, and the performance advantage keeps increasing with the increment of the data size of the task, indicating good time efficiency of Algorithm 2 when dealing with large size computation tasks. Figure 3b shows the experimental results of the service cost under different sizes of tasks. It can be seen that as the amount of task data increases, the service cost also increases. As expected, the cost of minPrice is the lowest among all comparison algorithms. Since minPrice is designed to minimize the service cost without consideration of other factors. However, the good performance of minPrice on the service cost is accomplished at the sacrifice of other important performance metrics. It can be seen that minPrice performs poorly in the completion delay (Figure 3a) and the resource utility (Figure 3c). Overall, the performance of minPrice is not satisfying. In the meantime, it is noticed that the proposed Algorithm 1 has the second-best performance on the service cost, and Algorithm 2 has the third-best service cost. Thus, the proposed Algorithms 1 and 2 achieve near-optimal performance on the service cost. From the simulation results in Figure 3c, it can be seen that the resource utility decreases with the increment of the data size of the task. The reason is that due to the maximum acceptable delay constraint of the task, when the data size of the task is large, it is difficult to complete the task timely with a few SVs. Instead, UV has to employ more SVs to execute sub-tasks simultaneously to meet the delay constraint. In doing so, each SV is assigned a relatively smaller size of sub-task, compared with that when the task size is small. Overall, UV employs more SVs to do the computation and each SV is assigned a smaller size of sub-task, thus degrading the resource utilization ratio of each SV. Moreover,

Figure 3: Performance comparisons under different sizes of tasks: (**a**) Completion delay vs Data size; (**b**) Service cost vs Data size; (**c**) Resource utility vs Data size. \begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Value** \\ \hline \(R\) & 27 Mpbs \\ \(r\) & 300 m \\ \(v_{min}\) & 16 m/s \\ \(v_{max}\) & 33 m/s \\ \(L_{follow}\) & 300 m \\ \(\Delta t\) & 0.5 s \\ \(D_{0}\) & [5, 30] MB \\ \(G_{0}\) & 100 cycles/bit \\ \(T_{0}^{max}\) & [3, 18] s \\ \(f_{j}\) & [0.5, 2] GHz \\ \(w_{j}(t_{0})\) & [0, 100] \\ \(C\) & \(1.2\times 10^{-7}\) \\ Length of road section & 2000 m \\ Width of single lane & 3.75 m \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Simulation parameter settings.