## 2 Related Work  
There have been numerous studies addressing offloading decisions in MEC systems, with most focusing on computation offloading from multiple users in a single- or multi-server MEC. Multi-user single-server MEC architectures involve a single MEC server integrated into an MBS or SBS. We further classify existing research as addressing either single-tier [9; 10; 11; 12] or multi-tier [13; 14; 15; 16] offloading of a computational task. [PERSON] et al. [9] used a game-theoretic approach to solve the problem of computing overhead, including completion time and energy consumption, whereas another study [10] used system utility as a quality of experience measurement based on device power consumption and completion time and solved the optimization problem by jointly addressing computational resources and offloading decisions. [PERSON] et al. [11] studied offloading and task caching to minimize the power consumption of user equipments, and another study [12] used the same scenario, given that MEC servers are only attached to one wireless access station. In Reference [12], the authors used Karush-Kuhn-Tucker (KKT) conditions to solve the problem of optimizing MD energy consumption. Moreover, there were similar studies in single MEC server such as References [30; 31; 32; 33]. For multi-tier offloading of computational tasks [13; 14; 15; 16], there can be one or more SBSs overlaid by an MSB, with the SBSs connected to the MBS by a wireless or wired network. In this system, only MBS is integrated with the MEC servers. [PERSON] et al. [13] investigated energy-efficient computation offloading in 5G HetNets comprising one MBS and one SBS by deploying an optimization problem to minimize the power consumption of the system while meeting latency limitations. The computing and communication demands on SBSs are smaller than those on an MBS. A previous study [14] optimized wireless backhaul bandwidth using resource allocation and computation-offloading decisions in order to minimize system computing overhead. Moreover, other studies [15; 16] considered a HetNet scenario created by multiple small cells and one macro cell, with [PERSON] et al. [15] assuming that each SBS was only connected with one user and proposing optimization of time and energy consumption in order to complete the computational tasks by combining offloading decisions, interference management, and resource allocation. [PERSON] et al. [16] proposed an algorithmic trade-off between energy consumption and completion time by combining offloading decisions and computational resource allocation in MDs. In these cases, installing a MEC at the MBS could save monetary costs but could potentially increase latency. Additionally, an IoT system involves a large number of computation-intensive tasks that can lead to resource bottlenecks at the single MEC server. In the case of multi-user multi-server MEC architectures, most studies focus on offloading onto single-tier multi-server MEC architectures [34; 35; 36; 37; 38; 39; 40]. In SBS and MBS systems equipped with MEC servers, the SBS and MBS work independently and considered equally important (the SBS is not connected to the MBS), enabling MDs to choose either the SBS or MBS for offload. A previous study [34] investigated use of a matching game to solve the optimization problem for total computing overhead, including energy consumption and completion time, by combining resource allocation and offloading decisions, and [PERSON] et al. [35] maximized the system-offloading utility by combining the computation-offloading decision and resource allocation. [PERSON] et al. [36] proposed a creative framework for computational task offloading to a MEC server in a software-defined ultra-dense network by optimizing the completion time of the computational tasks through their execution locally and on the edge cloud. [PERSON] et al. [37; 38] investigated offloading tasks in ultra-dense networks with multiple SBSs and one MBS.