## 2 Related Work  
The research community and various industries have attracted much interest in application development by introducing mobile devices' multicore CPUs and contemporary memory architectures. However, there is enough room for improving today's mobile devices' capacities and capabilities since some compute-intensive jobs cannot be performed on those mobile devices [13; 14]. Such resource-hungry applications divide the computational processes into small tasks and place them at a proximate edge server. The remote task execution reduces the latency with energy savings. In MEC, the locality of servers plays a vital role in workflows offloading [15]. Furthermore, user mobility is related to the mobile user's connection to an edge server. User mobility is critical when transferring from one base station to another after submitting a workload to the prior server. Task offloading results in longer delays, more energy consumption, and lower resource utilization [15]. The authors in [16] proposed a mechanism based on software defined networks to manage user mobility. The technique assigns task to the server with the smallest reported distance. The distance is calculated using signal strength. Real-time factors such as user position, velocity, and direction, are not considered. In Ref. [16], the authors consider the geographical locations of users within the cell's boundaries to calculate the distance [15]. However, as with the previous SDN method, it also ignores user trajectory data. The random waypoint model (RWP) is the simplest model that estimates user mobility [17]. The RWP model is limited in that it only considers user movement within the cell and only tracks users who are moving towards or away from the base station, making the handoff analysis and prediction decision more difficult to complete. Another RWP-based robust framework is proposed in [18] that endeavors user mobility when making offloading decisions. The framework is incapable of reducing the complexity of job execution or energy usage. The self-similar least action walk is a mobility model that analyses a mobile user's walking behaviors to predict his next likely destination [19]. It accurately predicts a user's location while walking within the same radio access network, making it perfect for task transfer from one device to another. Traditional mobile networks, such as mobile adhoc networks, have extensively employed and tested the mobility models outlined thus far. It is unclear how they will be deployed and how well they'll perform in small cell networks such as 5G or mobile edge networks. MEC servers typically prioritize the tasks for offloading depending on their computing power, channel usage, and extra processing requirements. However, the heterogeneous MEC system requires ongoing real-time task scheduling monitoring due to user mobility. To optimize the process, other performance factors such as energy consumption, latency, and system resource usage should also be considered. Another methodology for determining the availability of edge servers is the \"head-light model\", which has been used in various works on mobility. This model is used to determine where the edge servers are positioned in relation to the data center [20]. The model captures a zone that points in the direction of a traveling mobile user, such as a moving vehicle's headlight. The authors in [21] describe a compute offloading approach known as MobMig, which is designed to improve the work migration process by taking leverage of mobility. The MobMig balances the workload across the edge servers by identifying the overloaded nodes with no remaining capacity. With the mobility-aware and genetic algorithm (MAGA), which was recently detailed in [22], a novel offloading technique has been developed that is focused on decreasing offloading errors by exploiting genetic algorithms. MAGA optimizes offloading decisions with reduced mobile device power consumption and meeting task completion time requirements in FOG environments. MAGA can handle multisite offloading.