## 2 Related Work  
The emergence of intelligent applications produces the demand for computing. Hence, how to reduce the computation pressure in VEC under massive computation demand is an urgent problem to solve. As mentioned above, SeVs and ES can share their idle computing resource to help TaVs deal with their tasks and provide low-latency computing services. Generally, applications are not processed only on restricted local resources while SeVs or ES are available. Much research has focused on the auxiliary computation of TaV at these two ends. Due to the dynamic vehicular environment and the variation of available vehicular computing resources, it is a great challenge to design a practical task offloading mechanism to utilize vehicular computing resources via V2V links efficiently. The authors in [8] consider a three-node scenario where a source node wants to communicate with a destination node with the help of a relay node, where the relay node is for decoding and forwarding. The goal of the scenario is to optimize partitioning by a virtual queue model. Specifically, they present a stochastic modeling for V2V communication dynamics, an analytical model for characterizing the reliability of a V2V link, and an evaluation model to illustrate the computation reliability, which is defined as the probability that a vehicle successfully calculates a certain amount of data within a deadline. An offloading strategy for a vehicular fog computing-assisted platoon system is proposed in [9]. A task arrive at a TaV in the platoon according to the Poisson distribution. If the available resources in the platoon are sufficient, the TaV will request to offload the task to one SeV in the platoon. Otherwise, if the vehicular fog has sufficient computation resource, the TaV will transmit the task to the leader vehicle of the platoon, then the leader vehicle divides the task into the corresponding number of subtasks and transmits the subtasks to the corresponding SeVs in the vehicular fog one by one. The work in [10] considers a vehicle that has several computational tasks to process, but the limited onboard computational capability cannot satisfy the requirement of all tasks. It can offload some tasks to one neighboring vehicle with idle computing resources. In addition, the V2V communication link's dynamic and vehicles' computing resource allocation are considered. The work in [11] investigates the computation task allocation among vehicles with the help of a base station (BS) and proposes a distributed V2V computation offloading framework. In particular, the work formulates the task allocation problem as a sequential decision-making problem. Considering that vehicles with idle computing resources may not share their computing resources voluntarily, the work thus proposes a dynamic pricing scheme that motivates vehicles to contribute their computing resources according to the price they receive. The BS is used for information management and resource allocation. Similarly, the work in [12] leverages the ability of BSs to achieve inter-region task offloading and takes the role of BSs as the resource retailer in a proposed V2V trading paradigm. Based on the paradigm, the work proposes a distributed dynamic many-to-many task offloading framework to improve the edge resource utilization in VECNs while considering privately owned vehicles' individual serving and offloading intentions. ES deployed at a BS or a roadside unit (RSU) can also participate in computation to enhance the task processing capabilities of TaVs. On the one hand, the ES can participate in the computation process alone. The authors in work [13] propose an efficient partial computation offloading and adaptive task scheduling algorithm, where the system-wide profit problem is decoupled into three parts. To minimize the task transmission delay, they first develop an asymptotically optimal channel allocation discipline of all vehicles with a given offloading ratio.