## 5 Methodology
### Max-Min Fairness Oriented Offloading Approach
there is a clear tendency that with the increment of data size, the resource utility of Random degrades drastically and becomes the lowest among all comparison algorithms after the data size expands to 20 MB and above. It is understandable since a UV always randomly selects vehicles as SVs to unload sub-tasks, and the probability for the UV to find the optimal set for computation offloading is low, especially with a large-size task. Nonetheless, it is clearly seen that the proposed algorithms maintain the highest resource utility among all algorithms, with different data sizes of the task, which justifies the resource efficiency of the proposed Algorithms 1 and 2. ### Performance Comparisons under Different Number of Neighboring Vehicles

In this part, the data size of the task is set to 20 MB, and the maximum tolerable delay constraint is set to 12 s. The number of neighboring vehicles is randomly generated between [4; 14]. Figure 4a illustrates the completion delay of compared algorithms under varied numbers of neighboring vehicles. It is seen that the proposed Algorithm 2 achieves the shortest delay of all algorithms, and Algorithm 1 has the second shortest delay with different numbers of neighboring vehicles. The completion delay of both algorithms slowly increases with the number of neighboring vehicles, and the trend becomes faint when the number of neighboring vehicles becomes large enough. Figure 4b gives the service cost of all algorithms with different numbers of neighboring vehicles. Except for maxCom and Random, the rest of the four algorithms have similar performance trends in that the service cost decreases with the number of neighboring vehicles. The reason is that given more neighboring vehicles, UV is more likely to find SVs with low prices, thus reducing the cost. In the meantime, since UV inclines to choose SVs with low prices (whose computing capabilities may not be as strong as those with high prices), it may require extra time to complete the task computation, thus prolonging the completion delay. As explained above, the proposed Algorithms 1 and 2 favor choosing SVs with low prices which have a small volume of computing resources and treat SVs equally through Max-Min fairness allocation. In doing so, the idle resource on each SV can be utilized sufficiently and in turn, the overall resource utilization ratio is optimized. As Figure 4c shows, the proposed Algorithm 2 achieves the highest resource utility among all compared algorithms, and Algorithm 1 achieves the second best. Even when the number of vehicles around UV is small, the proposed algorithms can still manage to reach high resource utilities over 90%, which are far ahead of the rest of the algorithms. Moreover, it is seen that the resource utility slightly increases with the number of neighboring vehicles. Since UV has more potential SVs to choose from, SVs that have a small amount of resources and

Figure 4: Performance comparisons under different numbers of neighboring vehicles: (**a**) Completion delay vs Number of neighboring vehicles. (**b**) Service cost vs Number of neighboring vehicles. (**c**) Resource utility vs Number of neighboring vehicles. ask for low prices are selected as the final SVs. Their small amount of resources are equally treated and fully utilized with the proposed algorithms, thus improving the resource utility. ## 7 Conclusions

Computation offloading in vehicular edge computing is a new paradigm for intelligent transportation in IoVs. This paper aims at the problem of promoting vehicles on the road to provide efficient cooperation for completing computation tasks. To accurately quantify the capabilities of vehicles to execute the task, a fuzzy logic-based pricing strategy is designed to help each vehicle determine the most suitable price for lending its computing resource and providing cooperation. Based on the prices, a Max-Min fairness-oriented approach is proposed and two algorithms are presented to solve the problem.