## 2 Review of Related Works
### Network Slicing Solutions in 5G Networks  
This section discusses the existing works focused on implementing NS solutions. [PERSON] et al. in [20] utilized complex network theory methods to develop a service-oriented deployment approach to E2E-NS. In their work, NSRs are provisioned with slices by selecting the network node with the most favorable node importance among all candidate nodes at each time step. The importance of the node is calculated based on its degree and centrality. In addition, the authors then perform shortest path estimation to map all the selected nodes and create a network slice. Moreover, different slice provisioning strategies have also been formulated for uRLLC, eMBB, and mMTC service requests. [PERSON] et al. designed a network slice brokering agent named ONETS [21]. The primary goal of their work is the development of a slice admission framework based on the multi-armed bandit problem. Such work has shown an increase in service request acceptance and maximization of network multiplexing gains through rewards systems, outperforming conventional existing reinforcement learning algorithms in the process. In [22], [PERSON] et al. attempted to address a slice allocation problem brought about by a massive data influx in the network. They introduced a Glowworm Swarm-based Deer Hunting Optimization Algorithm (GS-DHOA) to perform slicing classification. Based on their results, the proposed method distinguished uRLLC, eMBB, and mMTC requests and provided the necessary slices with great accuracy compared to other candidate algorithms utilized in the simulations. The work in [23] proposed a NS strategy by implementing a multi-criteria decision-making (MCDM) method. The authors used a slice provisioning algorithm based on the VIKOR approach to solve a 5G core-network-slice-provisioning problem (5G-CNSP). They also used complex network theory and a two-stage heuristic approach. The MCDM process for node selection considered the computational capacity and bandwidth of the nodes, as well as the local and global node topologies as decision parameters. Based on their results, the authors concluded that the proposed slicing strategy provides high RE and acceptance rate in high network traffic while addressing network security requirements. [PERSON] et al. [24] proposed a multi-resource allocation protocol for resource distribution on multiple network tenants. Their work proposed an optimization framework named multi-resources network slicing or MURANES that is based on the ordered weighted average (OWA) operator. The proposed solution considers the congestion and demand for resources from tenants. Likewise, the authors formulated allocation rules for single and multi-resource allocation scenarios. Results indicated that the proposed framework could address the multi-resource allocation problem while considering traffic support and heterogeneous congestion. The work in [25] introduced a prediction-assisted adaptive network slice expansion algorithm utilizing the Holt-Winters (HW) prediction method. The authors aimed to predict various changes occurring within the network and then provide services through a VNF adaptive scaling strategy. Network slices were proactively deployed based on NSRs received using network traffic rate and available resource information as the main parameters. Moreover, implementing the proposed approach resulted in lower energy consumption rates and slice deployment costs within the network. [PERSON] et al. in [25] developed a RAN slicing framework for 5G networks aimed at maximizing bandwidth utilization and ensuring network QoS at the same time. Their paper focused on slice admission for NSRs through a joint slice association and bandwidth allocation approach. Slice admission policies for QoS optimization and user admissibility were then formulated, which increased the number of served users with the improved bandwidth consumption. [PERSON] et al. in [26] presented an application of Deep Q-Learning with the E2E-NS.