## 2 Related Work  
With its ability to be used in both edge and end applications and its features of low latency and high reliability, mobile edge computing is a widely adopted computing paradigm. MEC's core principle is the local processing of mobile application data at the network edge, close to the end user, or performing computation tasks (using, for example, a wireless network controller, a long term evolution (LTE) macro station, a small cell micro base station, etc.). As a result, the end-to-end delay is reduced and the resource scarcity of mobile devices is lessened. The MEC ecosystem still faces a number of significant issues that have not yet been adequately addressed as an emerging computing paradigm, and task offloading is just one of the difficulties in these areas. In order to achieve the best operational performance of the application and make efficient use of MEC resources, it is crucial to choose the appropriate task to offload to the appropriate MEC server at the appropriate moment [3]. Reference [4] proposed the network slicing and load balancing algorithm (VECSlicLB). The algorithm virtualizes the data plane of edge nodes by using the network slicing function. VECSlicLB algorithm is based on the decoupling of the control plane and the data plane to realize load balancing among edge nodes and improve resource utilization of edge nodes. A heuristic algorithm called Horae is suggested in the literature [5] when scheduling jobs for various users with precedence and geographic restrictions. However, it is simple for this heuristic method to enter a local optimum. Its shortcomings will shortly be made clear if it is used for multi-objective optimization in challenging situations such as those related to the internet of vehicles (IOV). In order to reduce the overall delay of all tasks, studies have been conducted on joint resource allocation and other optimization variables, includingjoint server allocation, transmission power allocation between users and MEC servers, MEC server computing resource allocation, time allocation, and channel allocation [6]. The fractional search method is optimized for computing resource allocation, but the authors do not take into account a coupling relationship between the offloading ratio and these resource allocations, and directly idealize it as a separate variable [7; 8; 9; 10; 11]. In reference [12], an effective low-complexity heuristic algorithm is proposed for a multi-user MEC supported wireless communication system to maximize the number of offloading tasks for all uses in uplink communication while maintaining the computing resources of MEC at an acceptable level. The proposed resource allocation strategy cannot ensure the rationality of the allocation, and this algorithm is established to increase the number of user tasks that are offloaded. Nevertheless, this algorithm offers a nearly optimal solution with little time overhead. With regard to the matching issue, the literature [13] considers the incentives for using the idle computing resources of vehicles as the edge nodes, and attempts to establish a differential pricing model based on different resource states. A gradient-based iterative resource allocation method (GRAIA) is developed. The issue of matching users and vehicles is then considered. An enhanced hierarchical clustering algorithm (HDBSACAN) is suggested in reference [14]. To locate suitable service vehicles for use as task vehicles and to increase task unloading effectiveness, an evaluation expression for nearby service vehicles is proposed. The literature [15] takes into account computing and communication resource allocation in conjunction with communication efficiency, combining delay and energy consumption to reduce overall network overhead. The issue is broken down into three smaller issues that are each solved separately using mathematical techniques, but this approach is very computationally complicated.