Influences of Weighting Techniques on TOPSIS-based Network Slice Selection Function

The vision of the fifth generation (5G) mobile systems can be conceived as a highly manageable networking environment that provides increased performance while supporting a variety of services with widely diverse requirements. In order to realize the above vision, network slicing has been proposed as a resource provisioning technique capable to meet these requirements with reduced operating costs, while opening new horizons for network efficiency. The aim of the Network Slice Selection Function (NSSF) is selecting the set of network instances serving the users, based on localized configuration, and other relevant information including radio access networks (RANs) performances in the registration domain. In this paper, NSSF based on Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) is proposed. Here, the network slices are considered as alternatives, and their performances indicators are contemplated as the criteria for decision making. The influences of various weighting techniques, including entropy, standard deviation, and variance, are analyzed through rank reversal phenomenon and computational complexity.