## 2 Related Works  
An increasing number of applications require low latency and low costs, such as autonomous driving, facial recognition, and AR. MEC, as a new computing model, is gaining more and more attention. [PERSON] et al. [11] combined an intelligent reflecting surface (IRS) with the MEC model and considered an IRS-aided multi-device MEC system, where each IoT device follows a binary offloading policy to minimize the total energy consumption of the IoT devices. [PERSON] et al. [12] proposed a novel STAR-RIS-assisted MEC system that extends the coverage range of wireless access points (APs) from half-space to full-space. [PERSON] et al. [13] discussed the problem of computational task offloading and resource allocation in remote areas lacking ground communication infrastructure through an air-ground integrated MEC system. [PERSON] et al. [14] introduced a new user-centric mobile edge computing (UCMEC) model that addresses the issues of resource allocation imbalance and network interference in traditional MEC architectures. Although the above studies have made improvements to task offloading in traditional MEC systems, they do not consider two crucial constraints in task offloading: task dependency and service caching. With the increasing complexity of applications, edge servers are constrained by limited resources and cannot cache all task services [2; 20]. Research on service caching mainly focuses on the joint optimization of service placement and task offloading. However, compared to task execution and offloading, service placement and updates often incur higher costs and time, negatively affecting system stability [20]. Moreover, due to the uncertainty of tasks and delay sensitivity, offloading schemes may cause congestion at certain execution nodes, significantly impacting the required offloading time [33]. Based on these factors, [PERSON] [20] proposed a task offloading scheme that decouples the time scales of task offloading and service deployment. Their method introduces a joint optimization framework for task offloading and service deployment under multiple constraints. However, the above-mentioned problem does not take into account the dependency between tasks. With the continuous development of applications, the vast majority of applications consist of sets of interdependent tasks, making the offloading of dependent tasks essential [15; 19]. [PERSON] et al. [22] addressed the cost minimization problem of dependent tasks under delay constraints in cloud environments. They set sub-delay constraints for each subtask based on the delay constraints and ranked them. Then, a heuristic ProLiS algorithm and a metaheuristic L-ACO algorithm were proposed to solve this problem. [PERSON] et al. [23] applied the aforementioned task ranking method to the MEC environment and proposed a two-stage heuristic algorithm to obtain the cost-minimized offloading scheme. [PERSON] et al. [26] focused on reducing delay and improving resource utilization for dependent tasks. To address this problem, they designed a Nash Equilibrium-based Joint Task Scheduling and Offloading Strategy (N-JSU). [PERSON] et al. [27] proposed a method based on an Enhanced Genetic Algorithm (EMGA), which considers task dependencies while optimizing multiple objectives. [PERSON] et al. [28] combine the prioritized queue strategy with a joint delay-quality dependence model, then transform the task assignment process into a tree search problem and solve it using a heuristic tree-based algorithm. However, they only consider task dependencies while neglecting service caching, limiting the applicability to real-world applications. In fact, the tasks of applications now not only need to determine the execution order based on their dependencies, but each subtask also requires specific services to be executed. Therefore, it is crucial to simultaneously consider both factors for task offloading. [PERSON] et al. [19] proposed a DP-based heuristic algorithm targeting task-completion-time minimization. [PERSON] et al.