# Comparative Analysis of 5G Mobile Communication Network Architectures
## 2 Previous Works  
Mobile cloud computing technology is the foundation of the MEC architecture. The technology attempts to overcome the limited computing resources of the mobile terminals of subscribers and to reduce power consumption by offloading the workload to a cloud located in a remote location. Research related to this area include work by [PERSON] et al. [11], who constructed a prototype of and image processing application that incorporated an offloading methodology, where the terminal workload is offloaded to a server. [PERSON] et al. [10] proposed a mobile cloud computing related framework named MAUI. Using the proposed framework, they defined a system to classify the offloading code, client-server connection, and offloading decision function based on optimization using linear programming and profiling function to improve the decision-making accuracy. [PERSON] et al. [9] proposed the CloneCloud framework, which enables more effective offloading through virtualization-based synchronization of program execution environment between the user terminal and server. More recent work focuses on establishing the application offloading process for augmented reality through mobile virtual reality [12]. The offloading methodology is another closely related topic that has received a lot of attention. Research on this subject is divided into two different areas, namely the single layer offloading methodology and multiple layer offloading methodology. The former focuses on developing methods to improve the efficiency of decision making for offloading between user terminal and edge cloud, while the latter focuses on solving edge cloud overload problem from a more macroscopic (system level) perspective. There are two different approaches to the single layer offloading methodology. The first approach involves the optimization of computing resources. Taking this perspective, [PERSON] et al. [13] used the Lyapunov optimization technique to solve the offloading decision problem for the purpose of minimizing terminal power consumption, subject to a dependency between the off-loadable modules. Recent work to implement this approach was published by [PERSON] and [PERSON] [14], who proposed a method to minimize the delay time through efficient allocation of the computing resources required for offloading in the virtualized network environment. Additionally, [PERSON] et al. [15] proposed a new concept called task caching, which solved the terminal power consumption minimization problem through mixed integer optimization, while considering MEC, storage resources, and execution time. The second approach to the single layer offloading methodology attempts to coordinate and optimize both computing and communication resources. In the work by [PERSON] et al. [16], the authors modeled the terminal task buffer queuing state, task processing state, and offloading transmission state based on a Markov decision process and proposed a solution to the delay time optimization problem using a one-dimensional searching algorithm. [PERSON] et al. [17] proposed a method to obtain an optimal trade-off condition between delay time and power consumption using the finite-state Markov chain model. In recent work by [PERSON] et al. [18], the authors proposed an algorithm to solve the computing and communication resource optimization problems using an offloading decision model, which is divided into computing resource model based on time division access and communication resource model based on orthogonal frequency division access. The multiple layer offloading methodology focuses on solving the MEC architecture overload from a more macroscopic perspective. [PERSON] and [PERSON] [19] proposed a new architecture called the mobile-central office re-architected as a data center (M-CORD) to address the MEC architecture's overload problem.