## 2 Related Work  
### Task Offloading Model  
Because task offloading operations for MEC primarily consist of local execution, overall offloading, and partial offloading, the current study uses two task offloading models: the overall offloading model and the partial offloading model [8]. The overall offloading model and the partial offloading model both have advantages and disadvantages; the overall offloading model is relatively simple, and the algorithm is simple to implement, but resource utilization is low. However, the partial offloading model is more complex, and the algorithm has higher requirements; however, if the strategy is reasonable, it can fully utilize the resources. The overall offload model assumes that the tasks to be offloaded cannot be partitioned, and that each task is independent of the other. The offload strategy needs to make the decision of local execution or offload execution for each task with a specific optimization goal and meet the needs of more applications by utilizing the computing and storage capacity of the edge server. Task offloading under the overall offloading model has been extensively studied in academia. [PERSON] et al. [9] studied an edge server placement at the edge of a base station scenario, looked at the task execution cost problem between multiple users and an edge server in this area, and set up a cost model to minimize the overall task execution cost as the goal for overall task offloading decision. [10] added wireless charging to the system and developed an edge model of a device with wireless charging modules with task decisions consisting of charging times and offloading decisions, using alternating direction multiplier decomposition techniques to reduce the strong coupling between unloading decisions and charging times and to overall speed up the mobile device performing the task by jointly optimizing each choice. [PERSON] et al. [11] studied the problem of collaboration between multiple edge server systems, finding that, when the server load is too high, the user experience suffers significantly, and that, by offloading the ineffective decisions to other systems, the overall system's effectiveness improves and the user experience improves. Regarding the improvement of task processing speed in the Internet of things, Table 1 lists the differences between the approach proposed in this paper and existing methods. Unlike the overall offload model, in the partial offload model, the application is able to offload some of the modules in the task for processing while the other part remains processed locally, and the two approaches can be performed in parallel [12]. The model assumes that each module of the application can be processed independently as a subtask. According to the backward and forward dependencies generated by the business logic, these subtasks must be processed in a certain order, and the offload strategy makes offload decisions for each subtask in a certain order, with the edge serv12 er and the mobile device processing the tasks simultaneously. The partial offload model is more flexible and can use all of the heterogeneous parallel resources in MEC to get the most out of the performance of mobile devices and edge servers. [PERSON] et al. [13] studied the MEC scenario of multiple edge servers collaborating on AR applications, first modeling the application as a directed acyclic graph (DAG) as a function of the connections between each module of the AR application task, and then investigating the performance of the particle swarm algorithm on real-time AR task offloading, despite the offloading scheme achieving optimal performance. The level of difficulty is high. As a result, the authors devised a heuristic algorithm to solve the problem, which produces a solution that is similar to particle swarm optimization but with less time complexity. [PERSON] et al.