of SFC while eliminating the consumption of resources (continued) 1 A Comprehensive Study on 5G Table 1.6 (continued) Reference Challenge Proposed algorithm and performance [154-156] In [154], an online heuristic topology-aware placement placement algorithm has been proposed for VNF. It is found to improve the efficiency of resource allocation in cloud-based environment and minimize the computation time. In [155], a decision-tree algorithm was implemented to find the optimal placement of the OpenFlow containerized SDN-VNFs. It is found to detect the optimal placement in short and constant delay that outperforms the cbench approach. In [156], a model for dynamic reliability-aware service placement was proposed and formulated as an infinite horizon Markov decision process (MDP) in order to minimize the cost of the cloud server placement [157] and VNF life cycle In [157], an online VNF scale-out/scale-in and dynamic [158] management live-migration scheduling algorithm was proposed to assign the VNF to an actual cloud or MEC hypervisor. The proposed algorithm is found to utilize the MEC and cloud resources efficiently and enable a high number of URLLC applications. In [158], a framework was proposed to combine the softwarized networks by SDN and NFV with ML-based management to manage and orchestrate all the different management aspects of NFV system including configuration, life cycle, performance, and fault management [139]. 1.4.3 Network Slicing The decoupling of data and control planes via SDN and the separation of the NFs from the underlying hardware via NFV permit the 5G networks to offer flexible, programmable, and self-contained E2E virtual network services [163]. Network slicing is defined by 3GPP as differentiated treatment for each tenant requirement based on the service level agreement (SLA) with the MNOs [164]. From UE perspective, network slicing represents the devices with similar design requirements gathered into a slice. On the other hand, network slicing from infrastructure perspective can be defined