## 1 Introduction  
In recent years, private and commercial 5G mobile networks have faced enormous mobile traffic due to a growth in the use of portable devices by consumers. In the near future, 5G technology is projected to assist many new enterprises and vertical sectors, including transportation, healthcare, and the energy supply chain. A highly sophisticated 5G architecture [1] is required for providing high-level, continuous, and reliable service to user equipment (UE) with a broad variety of needs. The next generation mobile networks (NGMN) by Alliance established the network slicing idea to achieve resource allocation for user devices with varying performance needs in 5G network settings [2]. Individual slices of physical infrastructure are created based on the grouping of requirements through a set of virtual network functions (VNFs) that have virtual resources, logical topology, traffic regulation, and node and link provisioning rules, as well as security monitoring parameters to guarantee the quality of service (QoS) [3]. The 5G system architecture that supports network slicing was previously established in the first edition of 5G normative standards and was authorized by the 3 rd Generation Partnership Project (3 GPP) [4]. According to the physical architecture, each logical network slice is generally made up of three key components: the radio access network (RAN) and the core network (CN) [5]. The logical networks are shown in Figure 1 with the components separated, assuming that the physical infrastructure is being used as a shared resource. RAN behavior was investigated in the work [6] by taking into account the criteria associated with the quality of service in order to provide a dependable service to the UE. [PERSON] et al. [7] dealt with network isolation in their study, and they allocated resources using traditional packet scheduling and dynamic resource block allocation techniques. In an orthogonal frequency division multiple access (OFDMA) context, network slicing and resource allocation based on a sub-carrier and power were performed in [8], where the maximization of the network summation rate was treated as the objective function, with the traffic rate of each slice assumed. In [9], network slices were found to be chains of virtual network functions and real resources to meet the varying needs of end users, leading a variety of studies on the issue of VNF deployment and orchestration. In [10], the VNF orchestration problem was framed as an integer linear program and addressed using a heuristic method, with the cost function consisting of the VNF deployment cost, the energy cost, the traffic cost, and the propagation delay cost. Another solution, known as WiNE (wireless network embedding) [11], was developed to address the issue of VNF placement in large-scale installations. For the purpose of solving the VNF placement issue according to requests, WiNE examines three various situations, including linear VNF requests, branching VNF requests, and VNF requests containing loops, among others. Using software-defined networking (SDN) and VNF together was detailed in [12] to provide network slicing, which also covered the underlying principles, designs, and challenges of the two techniques. Recent research shows that VNF-based network slicing is superior to SDN when it comes to increasing the profit and maximizing the utilization of resources. A thorough examination of the strategies for providing logical network nodes and connections from physical resources, which is referred to as the virtual network embedding (VNE) issue, is provided in [13]. VNE is also known as network slice provisioning, and [14] provides a framework for allocating physical resources to logical networks. According to previous network slicing research findings, network slice provisioning becomes difficult in many ways, including ensuring the highest possible acceptance ratio, secure data transmission, and a low latency, and providing service for a diverse range of user requirements [15; 16].