# Mobility-Enabled Edge Server Selection for Multi-User Composite Services
## 6 Related Work  
The problem of multi-user edge server selection in mobile edge computing has been extensively investigated in the past few years in many research tracks [7; 9; 25; 26; 27]. These papers elaborated on how to select edge servers for multiple users from different aspects. In [25], the authors studied the multi-user computation offloading problem for mobile-edge cloud computing in a multi-channel wireless interference environment. A multi-user computation offloading game was executed to decide whether the computing task was offloaded to an edge server. By the proposed approach, a Nash equilibrium was achieved, and the total offloading latency and energy consumption were minimized. The authors of [9] proposed how to dispatch and schedule the jobs in edge-cloud systems. They derived the first online job dispatching and scheduling algorithm, called _OnDisc_, which determines whether each job is to be processed locally at its device or offloaded to a server. The total weighted response time over all jobs was minimized by the proposed approach. Rather than rely on remote cloud servers, the multi-device task scheduling strategy for the ad hoc-based mobile edge computing system was proposed in [26]. The authors developed a multi-device distributed task scheduling game, which can make the task be offloaded to an optimal mobile device. When this game arrives at a Nash equilibrium possessing a finite improvement property, the overhead in term of time latency, energy consumption, and monetary cost is optimized. In [7], the authors modeled the user allocation problem as a bin packing problem, and the user allocation problem was solved as a series of connected integer linear programs. While satisfying the capacity constraint and proximity constraint, users were allocated to optimal hired edge servers to maximize the allocated number of users and minimize the hired number of edge servers. Considering the limited computing capacities and large amounts of peak load, a hierarchical edge cloud architecture was designed in [27] instead of using a flat collection of edge cloud servers. They developed workload placement algorithms that adaptively place users' workloads among different tiers of servers and decide how much computational capacity  
Figure 8: Impact of downtime on the total waiting time. is provisioned to execute each program, in order to make the average program execution latency minimized. Although the aforementioned papers achieved the goal of selecting optimal servers for many users, their research assumed that the users did not move. In real life, the users would move along a road over a period of time. Our research targeted much more realistic edge computing scenarios where the users move. At the same time, due to the limited coverage of a single edge server and the mobility of a user, this may lead to a significant decline in network performance or even disruption of ongoing mobile devices [5]. It would be difficult to ensure service continuity [28]. A preliminary work on mobility-driven service migration based on the Markov Decision Process (MDPs) was given in [29], which mainly considered one-dimensional (1D) mobility patterns with a specifically-defined cost function. Then, two-dimensions (2D) were proposed in [30] with distance-based MDP, which is a very realistic case compared to 1D mobility. They both computed a service migration policy to decide when the service migration was executed. In [5], the authors discussed the cutting-edge research efforts on service migration in MEC [28; 31; 32; 33] and the process of service migration [34; 35]. However, the authors only considered the service migration of a single user in a cellular system. Due to the limited capacity of an edge server, many users may influence each other when they select edge servers. Our work tackled the multi-user edge server selection problem in scenarios with a known mobile path and services. We also realistically addressed this problem with respect to proximity constraints and capacity constraints.