# A Novel Approach to Multi-Provider Network Slice Selector for 5G and Future Communication Systems
## 4 Intelligent Edge Computing
### Slice Composition with Machine Learning  
The allocation of resources for composing a slice must support the _slice_ lifecycle management function [59], which, in turn, must meet the requirements specified in the SLA and the QoS and QoE rules in compliance with latency metrics, throughput and capacity of available resources [60]. Considering the characteristic of distributed computing [61], virtual systems and functions, the variable dynamics of the network, as well as the dynamicity and variance of the parameters over time, make the orchestration of resources for slice composition a complex and appropriate task for applying a solution based on AI [62]. However, a premise to apply AI techniques is the existence of a mathematical model, the knowledge of an expert, or a set of data. Given the inexistence of the first two options in this work, it was necessary to build or search for datasets that portray the slice efficiency metrics or even use data streaming for application to AI models. Hence, monitoring a network's incoming traffic or the historical behavior of slices in the form of raw data by streaming can represent the behavior of the network in terms of QoS and serve as a basis for obtaining optimal models. The research interest in resource allocation usually focuses on how to implement slice instances according to the description of resource requirements [63] and following the service agreement. In this sense, a methodology for collecting and analyzing data from a network that is flexible enough to support the interactive and iterative model for producing ML Models is validated. Thus, a good strategy is to establish a data format to train various AI models and perform slice recognition and recommendations given the flow generated by the client. The research interest in resource allocation focuses on how to implement virtual network instances under shared physical resources. End-to-end NS for 5G mobile networks using ML has intensively been studied [5; 64; 65; 60; 66; 67]. One of the most important issues related to the use of slice by attribution performed from ML is to guarantee the generation of optimal models to avoid the sub-utilization of resources as well as the SLA breaking. Establishing metrics and an optimal slice recovery mechanism for deterministic demands was the starting point in the work of [PERSON] [68], in which he sought to build the ideal solution to be used as a reference in other algorithms. In stochastic demands, the option of optimization mechanisms that generally have slow convergence is initially adopted, with the use of ML as a future solution. Establishing a dataset that reflects an optimal solution for guiding ML is an appropriate scenario for building supervised learning models. However, the diversity of scenarios and demands of the clients of the networks make standardized definition a complex task. Thus, exploring types of learning that are not dependent on datasets containing the optimal model classifier label is presented as a plausible and viable alternative. Hence, for example, unsupervised learning, recurrent, and convolutional neural network models are adopted in the construction of models for solutions related to NS. In the work by [PERSON] [69], neural networks of the Long-Short Term Memory (LSTM) type were used for slice provisioning in a simple mechanism with only four parameters contributing to the SLA, being (\(Rs\), \(Ks\), \(Ws\) and \(Ds\)), where \(Rs\) is the average ratio of resource utilization, where \(Rs\)\(\epsilon\)\([0,1]\). To ensure tolerance to the network operator, \(Ks\) measures the maximum standard deviation of resource sharing. In turn, \(Ws\) is a window of time during which the average is calculated and where the sharing of resources plus the deviation must be guaranteed. Finally, \(Ds\) specifies the lifetime duration of the slice. The training of the neural network was conducted by using data from 24 h of running a traffic simulation in the NS-3 Network Simulator [70].