# A Novel Approach to Multi-Provider Network Slice Selector for 5G and Future Communication Systems
## 3 Related Work
Relevant scientific work performed on NSSF is discussed in the following and summarized in Table 1.  
In addition to the Virtual Network Function (VNF) for selecting slices, [PERSON], [PERSON], [PERSON] and [PERSON] [34] argue that the literature shows that a management system is also required to provide, update, and control the VNFs of the physical layer that makes up the slices. They focused on the provision and deployment of Data Plane Network Slices and the selection of a proper slice by taking identification parameters from the user equipment. They also confirmed that traffic control rules can be deployed with minimal resources without affecting the efficiency of the Data Plane slices. As the proposed system evolves, a new degree of automation can be developed with a monitoring agent that can supervise the status of SPGW-U traffic in real-time. The works in [8; 35] proposed NSSF based on Technique for Order Preference by Similarity to Ideal Solution (TOPSIS). According to the principles of network division, a mobile user/terminal may choose from several connectivity alternatives available based on criteria related to slicing performance, service requirements, and user preferences. NSSF is perceived as a logical evolution from the ABC concept to 5G and beyond mobile systems. NSSF is modeled as a Multiple-Criteria Decision-Making (MCDM) problem exploring the principle of TOPSIS for classifying the available slices based on their attributes and weights. TOPSIS is a widely accepted decision-making tool, considering its understandable logic, algorithmic logic, and mathematical form. However, it fails to provide consistent results due to the phenomenon of rank reversibility. As for the TOPSIS methods, the results in [8] demonstrated that the standard deviation weighting technique (SD-TOPSIS) presents significantly better performances regarding rank reversibility, but it has poor performance in terms of complexity. In this sense, they suggested applying the entropy weighting technique (E-TOPSIS), especially when faced with characteristics such as the fine granularity of slices. The authors also concluded that the improvements in TOPSIS methods are closely related to alternative methods for normalization and ranking phases, especially when managing practical implementation situations. As for future work, the intention is to analyze the influence of several alternative standardization techniques and classification methods on the NSSF performance. Besides, the results from [35] consider the three stages of the decision-making process (normalization, weighting, and ranking), and show that proposed alternative techniques, such as linear normalization (MAX-MIN), the weighting of variance, and binary classification alternatives, presented a positive influence by significantly reducing classification reversibility and computational complexity among the tested scenarios. This justifies the need to consider MCDM methods as a potential solution to the network slice selection problem in 5G mobile systems and their future generations. As for future work, the performance of other MCDM algorithms should be analyzed in terms of ranking reversibility. The work in [36] presented a new approach that aimed at increasing service utilization and the efficiency of network slices, since slice selection is one of the key elements of 5G Network Slicing. In the proposed method, each network slice is considered as a different service and is represented by a website system with its own database. Then, the GET method is used to link the user equipment to multiple systems at the same time, focusing mainly on passing the required parameters by URL. By using this approach, Multiple-Service UE will be able to connect simultaneously to several networks, obtaining a session on the related networks for a specific time.