# A Novel Approach to Multi-Provider Network Slice Selector for 5G and Future Communication Systems
## 4 Intelligent Edge Computing  
Edge computing refers to a broad set of techniques designed to move computing and storage out of the remote central cloud (public or private) and closer to the source of data [41]. It exists to increase the computational capacity of the network. Supposedly, the next generation networks may support the connection density of more than 1 M devices/km\({}^{2}\). The introduction of a large number of devices in the networks hinders the processing of a large amount of data within low latency [42]. New applications, such as tactile Internet services, may also require an extremely high data rate, lower latency, and extended reliability. To facilitate these types of services, edge computing is incorporated as a technique to process most of the data at the network edge, instead of transferring it to the cloud network that is far away. Within the broad topic of edge computing, ETSI Multi-Access Edge Computing (MEC) is the widely accepted standard that must be met for a technology to be considered edge computing [43]. Its standards are guided by the following principles [29; 44]:  
* Edge computing must have a virtualization platform to be considered MEC (ETSI uses its NFV architecture in the standard);
* MEC can be deployed at radio nodes, aggregation points, or collocated with the Core network functions;
* APIs in a MEC environment must be simple, controllable, and, if possible, reusable for other tasks;
* Since the computing, storage, and network resources required by an MEC application may not match what is available in a node, an MEC network requires lifecycle management for the entire application system to handle these variables correctly;
* MEC systems must be able to relocate a high-end mobile application running on an external cloud to an MEC host and vice versa, meeting all the application requirements (ETSI admits that this principle needs further research). \begin{table}
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**Work** & **Contribution** & **Limitations** & **Future Works** \\ \hline [PERSON] (2020) [37] & A multicriteria decision framework for the optimal selection of Edge Points of Presence (EPoPs) to deploy a network slice. Results indicate the relevance of the proposed two-stage method in meeting the user’s hard and soft requirements, allowing communication between slices and optimal resource allocation from the providers. & High cost of deployment & Implement a distributed PoP selection mechanism, improving service discovery and cross-slice communication \\ \hline [PERSON] et al. (2020) [38] & A Genetic Algorithm that can achieve satisfactory results in the maximization of user’s Satisfaction Degree (SD) in the E2E network slicing problem. This method obtained better access and transmission performance when compared to traditional selection methods based on the Received Signal Strength (RSS) or greedy algorithms. & Lack of real world implementation & It is reasonable to propose a real world experiment to validate the GA algorithm \\ \hline [PERSON] et al. (2021) [40] & A dynamic slice selection technique that learns to recognize the rough situation and the mapping between current situation and the future slice. The Bayesian Attractor Model (BAM) is used to achieve consistent recognition, as well as the Dirichlet Process Mixture Model (DPMM) to achieve automatic attractor construction. Situations mapping is also automatically learned by using feedback. & Problems such as the bit rate drop should be predicted in advance and the slices should be switched in advance & To incorporate the control lag in the slice selection prediction mechanism \\ \hline [PERSON] et al. (2022) [39] & The use of hybrid machine learning algorithms and MCDM methods as a solution for the 5G network slice selection in IoT scenarios. The proposed solution proved to be efficient and the adopted MCDM methods show a similar performance. & New experiments considering different scenarios and further development of the proposed algorithms \\ \hline \end{tabular}
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