# A Novel Approach to Multi-Provider Network Slice Selector for 5G and Future Communication Systems
## 3 Related Work
As future work, the authors suggested providing the user with a PURE connection to these networks, meaning that by passing the parameters to the new system, the users can be registered to all existing systems, benefiting from their full capabilities, instead of having a temporary connection as observed in the present work. [PERSON] [37] presented a multicriteria decision framework for the optimal selection of Edge Points of Presence (EPoPs) to deploy a network slice. The EPOP Selecting Framework is composed of the following three main components: (i) the Service Registry, which contains the required KPI values for each EPoP candidate; (ii) the Filtering Engine, which is responsible for the initial filtering of the candidate EPoPs according to the user's requirements; and (3) the PoP Ranking Mechanism, which selects the most appropriate EPoP for slice deployment based on the multi-criteria Fuzzy Analytic Hierarchy Process (FAHP) method. The proposed framework was assessed under a realistic scenario in comparison with simple filtering and the single-object (FAHP) approach. The results indicate the relevance of the proposed two-stage method in meeting the user's requirements for hardware and software, allowing for the communication between slices and optimal resource allocation from the provider's point of view. Future work should include the implementation of a distributed selection mechanism to improve service discovery and communication between slices, hence reducing the cost of deployment. The work in [38] focuses on maximizing system resource utilization while guaranteeing the satisfaction degree among users by exploring the E2E network slicing problem. Using theoretical analysis, the authors proved that this is a NP-hard problem, and then they proposed a Genetic Algorithm (GA) to solve the optimization problem. A simulation experiment was conducted to validate the proposed GA algorithm, showing that this method obtained better access and transmission performance when compared to traditional selection methods based on the Received Signal Strength (RSS) or greedy algorithms. The authors did not state future work intentions, but it is reasonable to suggest the conduction of real-world experiments using the proposed GA algorithm to validate the simulation results from this work. [PERSON] et al. [39] presented a solution for 5G network slice selection in IoT scenarios. It uses Edge Computing resources with the application of hybrid machine learning algorithms and MCDM methods to permit IoT applications to better adapt data processing and routing, providing a better experience for users. From the results, the proposed solution proved to be efficient and the adopted MCDM methods show a similar performance. This demonstrates the high level of flexibility in the ranking of alternatives for the proposed methods as a function of the adopted weights. The authors state that in future work it is necessary to consider different scenarios for new experiments, in addition to further development in the proposed algorithms. [PERSON] et al. [40] proposed dynamic slice selection by learning to recognize the rough situation and the mapping between the current situation and the future slice. The Bayesian Attractor Model (BAM) was used to achieve consistent recognition, as well as the Dirichlet Process Mixture Model (DPMM) to achieve automatic attractor construction. Situations mapping was also automatically learned using feedback. The video streaming situation was used for the application of dynamic slice selection and the results show that the proposed method can maintain a high quality of video streaming while reducing slice changes. Extended BAM can be used to reduce the number of slice changes while reducing the quality degradation of video streaming. Additionally, the integration and deletion of attractors was used to maintain only the necessary number of attractors. Future work should include control lag as a parameter for the slice selection prediction mechanism.