## 1 Introduction  
In MEC, 5G cellular users, intelligent sensors, surveillance systems, and other smart devices are connected to the network edge [1]. Handling mobile users at the network edge effectively uses spectral resources, lowers response time, and saves energy costs. By 2022, it is anticipated that global mobile data traffic would have increased by 77 exabytes per month [2]. 3D modelling, augmented reality, online gaming, high-definition multimedia processing, streaming, and machine learning-based applications are just a few examples of the computationally demanding applications that consume enormous amounts of resources and generate a large quantity of data [3]. In recent years, technological advances in multi-core chip and memory architectures have made it possible for mobile devices to perform admirably. However, these mobile devices are still constrained with limited computational power, battery life, and memory. Edge computing overcomes these limitations by offering computational offloading [4, 5]. In mobile edge networks, the intermediate servers between user and core network exist collocated at the small cell base stations that offer computation and storage services for the mobile devices. This service provisioning mechanism reducesthe load on data centers with optimized response time [6]. Nevertheless, the issue of higher latency has been addressed to some extent, but some applications continue to have strict latency and computational limits [7] and are offloaded to nearby MEC servers, as illustrated in Figure 1.  
Optimal server selection enhances the success of computation offloading in MEC. Latency, energy usage, and resource utilization are all indicators of optimal server performance [8; 9]. Some research efforts have looked at combining the optimization of two or more of the factors mentioned above. User mobility has also been cited as a crucial factor in offloading decisions in a number of studies [10]. Different machine learning-based prediction methods have been created to anticipate a mobile user's future position based on their prior mobility pattern. The best MEC server at the anticipated location is used for task assignment. Reinforcement learning, which has a lower training complexity than supervised learning and unsupervised learning, is used in the majority of studies applying machine learning for task offloading [11]. Nonetheless, the reinforcement learning techniques have a significant temporal complexity caused by their repetitive hit and try processes [11]. In addition, the accuracy of user position prediction is inconsistent, resulting in suboptimal task assignment when real-time mobility factors such as user velocity, location and direction are ignored [12]. For example, dynamic mobility-aware partial offloading (DMPO) is a model for reducing the length of the offloading path after the movement of user equipment. Prediction is carried out considering the user tasks and mobility paths in the near future [8]. Task-assignment with optimal mobility (TAOM) assigns tasks based on the shortest possible pre-calculated execution time. It is appropriate for workflows with a small number of users, but not practicable for jobs with a big number of users [9]. The TAOM claims to be simple and has low latency. On the other hand, the DMPO predicts energy-efficient servers. However, both strategies deal with user mobility and task offloading individually, affecting latency and energy. DMPO's mobility model is one-dimensional and does not predict user orientation. As a result, we propose COME-UP, a framework that utilizes the latitude, longitude, and user velocity as input parameters. Further, an optimized edge server with the lowest latency, energy consumption, and highest server utilization is selected. For model training, we used the Luxembourg SUMO Traffic (LuST) data set of 1000 vehicular users that is freely available online [10]. We selected TAOM as the baseline technique and DMPO for comparison with the proposed model.