ser services was proposed. The simulation results in [189] achieving a near-optimal performance for the admission policy. On the other hand, Bega et al. [190] presented an analytical tool based on the convolutional neural network in order to manage the decisions of the slice orchestrator. In [191], a DRL algorithm was proposed for RAN slicing in order to achieve the optimal resource allocation regardless of the slice number. The simulation results in [191] were found to maximize the number of slices that fulfills the requirements of each application. Meanwhile, in [196], a deep Q-learning algorithm was proposed for the RAN slicing in order to optimize the resource reservation. In [195], a DL algorithm with a RL method was proposed for RAN slicing to predict the traffic volume for resource allocation and scheduling. 1.4.5 Multi-Access Edge Computing (MEC) With the arrival of IoT and the massive surge of applications, an evolution toward edge computing was mandated to alleviate the various challenges of cloud com- puting involving ultralow latency, network congestion, high QoE, energy savings, and scalability [64, 67]. Edge computing brings the computational resources in the proximity of end-users and can be located anywhere in the RAN to connect the end-users with the core network [165]. For instance, in [198], edge computing servers were considered to be inside a femtocell or the device itself. Consequently, several benefits can be gained by edge computing such as low latency, real-time processing, location awareness, high scalability, availability and reliability, high security and data privacy, and saving energy and cost [64-66]. Under the umbrella of edge computing, several computing paradigms were proposed such as mobile cloud computing (MCC), cloudlet, fog computing, and mobile edge or multi-access edge computing (MEC) [64]. In this section, we address MEC because it is the standardized technology for mobile networks. MEC transfers the cloud capabilities, including computing, storage, and net- working resour