## 2 Related Work
### Traditional Computation Offloading  
First of all, a class of existing task offloading optimization algorithms in practical applications focuses on task dependency. [PERSON] et al. [18] focused on a dependent task assignment problem over multiple mobile terminal devices (MTDs). In [19], an efficient partitioned search method was implemented to obtain optimal solutions for task offloading policies and resource allocation under task-dependent models. Moreover, without considering task dependency, determining whether to offload tasks to edge computing nodes (ECNs) and how to allocate computing resources in ECNs based on the performance requirements for computing offloads are hot topics among current researchers. The three mainstream research directions under this scope are as follows. The first one is to explore task offloading decision schemes that minimize task execution delay [20; 21]. [PERSON] et al. [22] designed a heat prediction method to analyze the dynamics of urban heat zones, aided by the design of a non-cooperative game-theoretic strategy selection based on regret-matching to achieve the minimum time delay. [PERSON], [PERSON] et al. [23] proposed a JSCO algorithm to search for the solution to the optimization problem in a distributed manner with less overhead, using the integrated task processing delay as the performance metric. In [13], [PERSON], [PERSON] al. proposed a novel offload approach, OAMC, which takes into account the dynamic changes of mobile applications. This approach aims to reduce the migration number and overall data movement while minimizing the turnaround time of mobile applications. Secondly, plenty of studies [24; 25; 26; 27] focus on obtaining task offloading decisions in edge computing networks that minimize energy consumption, which is affected by the amount of offloading computations, the MTD-MEC distance, channel conditions, application type, compression efficiency, _etc._ [PERSON] et al. [26] jointly considered the priority of certain MTDs and aimed to minimize overall power consumption. Third, the authors of [28; 29] assumed that the system can gain a certain amount of revenue by completing the task, and the goal is to find the offloading decision that maximizes the revenue. [PERSON] et al. [30] synthetically explored delay-sensitive and delay-tolerant edge services while ensuring the maximization of quality of experience (QoE) over an extended period using the Lyapunov method. In addition to the challenges commonly addressed by traditional optimization methods, some researchers [31] have chosen to apply intelligent optimization algorithms to determine the optimal unloading decision. In [27], the authors developed a new adaptive inertia weight-based particle swarm optimization (NAIWPSO) algorithm to minimize the energy consumption of MTDs while considering the channel constraint conditions during task offloading. [PERSON] et al. [32] proposed an algorithm, named EIPSO, which is based on a particle swarm optimization-based algorithm. [PERSON] et al. [33] designed a multi-objective intelligent optimization algorithm based on the genetic algorithm. Machine learning and deep learning algorithms are now extraordinarily prevalent in the research field. [PERSON], [PERSON] et al. [13] applied a machine learning (ML) algorithm called matrix complementation to develop two novel offloading methods, S-OAMC and G-OAMC, which enhance scalability and identify offloading decisions with low turnaround times. In [34], various machine learning techniques for communication, network, and security components of future 6G environments in vehicular networks were profiled, and methods and directions for implementing artificial intelligence in future 6G vehicular networks were envisioned. However, machine learning algorithms typically require significant amounts of training data and cannot be directly computed. They rely on training with large datasets to learn patterns and make predictions.