# Mobile Personal Multi-Access Edge Computing Architecture Composed of Individual User Devices
## 1 Introduction  
The proliferation of high-performance user mobile devices and advances in network technology have led to the explosion of computational-intensive, delay-sensitive mobile application services such as real-time online games, virtual reality (VR) and augmented reality (AR) [1; 2; 3; 4; 5; 6]. For example, AR services provide mobile users with an experience of interacting with the real world by adding computer-generated perceptual information to objects that exist in the real world. Since these services should quickly process data collected from cameras and sensors on user mobile devices, AR services require high computing power. Due to these changes in service and application requirements, mobile terminal technology at the edge of the network is also changing rapidly [7; 8; 9]. However, due to the limited battery capacity and computational processing power of the mobile user devices, mobile users cannot be provided with these services efficiently [10; 11; 12]. To meet these service and application requirements, the computing paradigm is widely used to deliver these services to mobile users using cloud computing such as Google Cloud Platform (GCP) and Amazon Elastic Compute Cloud (EC2) [13; 14; 15]. Cloud computing, which provides infrastructure as a service (IaaS), platform as a service (PaaS) and software as a service (SaaS) by clustering multiple data centers can deliver high-performance services that require large amounts of computation. In addition,due to the various research and technology developments of the cloud computing, cloud computing can provide well-established deployment models and application platforms. However, the existing cloud computing architecture has long delays due to the propagation distance from the mobile user device to cloud data center. Also, the enormous amount of data exchanged between the mobile device and central cloud data center causes the data tsunami that saturates the backhaul network. These problems make it difficult to provide mobile users with the fifth-generation cellular network (5G) services that require high reliability and low latency such as ultra-reliable and low-latency communication (URLLC). Various kinds of network architectures and efficient schemes are emerging to solve these problems [16; 17; 18; 19]. In particular, the small cloud computing containing the various computing functions close to the mobile user is the subject of active research [20; 21; 22; 23; 24]. The concept of fog computing, the service infrastructure that provides services similar to the cloud computing, is proposed by Cisco but does not act as a central server and acts like the cloud [25; 26; 27]. In other words, fog computing extends cloud computing to the edge to securely control and manage domain-specific hardware, storage, and network capabilities within the domain and secures powerful data-processing applications across the domain. Similarly, the concept of MEC proposed by European Telecommunications Standard Institute (ETSI) provides the powerful cloud computing functionalities in the radio access network (RAN) [28]. In other words, edge computing can provide services with relatively low latency, but the edge server has limited computing resources, which makes it difficult to perform tasks requiring high throughput. Although MEC-related studies are being actively carried out, there is a lack of research on edge cluster structures for efficient working offload [29; 30; 31; 32; 33; 34]. However, when the edge server becomes congested due to frequent task offloading requests from mobile users, the advantages of edge computing that provide ultra-low latency response by performing computing tasks in close proximity to the mobile user disappear. Accordingly, there is a need for an efficient tasking offloading scheme in edge computing environments. To solve these problems, this paper aims to study how to support efficient task offloading by using user mobile device as edge computing host in the MEC environment. The rest of the paper is organized as follows.