## 2 Related Work  
Existing research on task offloading and resource allocation has focused on various objectives. Some studies aim to minimize task completion time in the system. [PERSON] et al. [11] designed a subgradient-based algorithm to reduce latency for mobile devices with divisible compression tasks. [PERSON] et al. [17] minimized task execution time with the help of helpers in a TDMA system, using relaxation-based and decoupling-based approaches to obtain a suboptimal solution. [PERSON] et al. [18] jointly optimized beamforming and resource allocation to minimize the maximal delay encountered by users in the mmWave MEC system. [PERSON] et al. [19] incorporated cloud and mobile edge computing and formulated a computation delay minimization problem with limited bandwidth resources. [PERSON] et al. [20] minimized service delay with a user-mobility prediction model in heterogeneous networks. [PERSON] and [PERSON] [21] minimized total task duration in software-defined ultradense networks. [PERSON] and [PERSON] [22] proposed a deep reinforcement learning (DRL) method to decide on the task offloading issue and introduced computation and transmission queues to model delays encountered in the MEC system. Edge computing resources were equally allocated for tasks at edge nodes, which implied that the computing resources allotted to current tasks would be reduced with the arrival of new tasks. In addition, part of the current literature focuses on designs that minimize energy consumption in MEC systems. [PERSON] et al. [23] studied an energy-efficient wireless resource allocation policy for computation offloading in both TDMA and orthogonal frequency-division multiple access (OFDMA) systems. [PERSON] et al. [24] jointly optimized bandwidth and computation resource allocation to minimize UDs' expected energy consumption, considering caching. The initial problem was formulated as an MINLP, and the caching decision subproblem was decoupled and solved by a learning-based deep neural network. [PERSON] et al. [25] designed a DRL method to learn a joint offloading decision and edge-computing resource allocation policy to minimize energy consumption. [PERSON] et al. [26] incorporated the Monte Carlo tree search (MCTS) algorithm with a deep neural network to learn the optimal bandwidth and computing resource allocation policy. [PERSON] et al. [27] investigated the offloading and resource allocation problem for tasks under the general dependency model. An actor-critic-based DRL method was proposed to generate the offloading actions. Furthermore, there have been several efforts to design the task offloading and resource allocation schemes based on other optimization goals. [PERSON] et al. [28] established a Stackelberg game based incentive mechanism to motivate BS to allocate resources more reasonably. [PERSON] and [PERSON] [16] modeled the computation rate maximization problem in wireless-powered TDMA edge networks as an MINLP problem, which was further decoupled and solved with the ADMM-based method and coordinate descent (CD) method. [PERSON] et al. [29] decoupled the computation rate maximization problem into a computation offloading decision subproblem and a wireless resource allocation subproblem. They solved the offloading decision subproblem with a DNN method and the wireless resource allocation subproblem with a one-dimensional bisection search method. Furthermore, [PERSON] et al. [30] adopted the Lyapunov optimization theory to decompose the maximization problem of the long-term weighted sum of the computation rate of all devices into a single-step optimization problem solved with an actor-critic-based deep reinforcement learning method. While some existing research has considered caching [24; 31; 2] in edge networks and user mobility issues [13; 20; 32], it falls outside the scope of this paper. The characteristics of part of the discussed works are summarized in Table 1. However, they all ignore that in resource allocation, the allocation share for small-data-volume tasks can be crowded out by large-data-volume tasks.