## 3 Integrating Machine Learning into Edge Sliced Networks
### Machine Learning Applied to Network Slicing
; (2) optimal weighted feature extraction; and (3) slicing classification to classify the network slices as eMBB, mMTC, or URLLC by a hybrid classifier using deep neural networks. As part of their experimental process, they compared their algorithm with other ML algorithms and proved that the combination of MI and deep learning algorithms outperforms the conventional ML models by up to 45%. [PERSON]. [44] presented a conference paper on an ML aided network slicing algorithm which predicts if a service provider will be able to fulfil a new network slice request given the conditions of the channel and the allocated resources. The main aim of this study is to predict the channel conditions in the near future using the Long Short-Term Memory algorithm. Experimental results showed that their DL based algorithm is able to reduce the number of false positive allocations by 75%. [PERSON]. [45] also focused on integrating ML algorithms, SDN, and NFV to build a comprehensive 5G architecture and an experimental framework. In this study, the authors proposed an approach for clustering and classifying mobile applications at an early stage, then each application is preserved a relevant bandwidth controlled by an SDN controller to guarantee that the network is working at high efficiency. The proposed framework is very promising as it is able to classify a high-quality YouTube video successfully to the correct slice, i.e., selected cluster. It also includes applications that require low latency and high bandwidth and, as a result, the UE played the video smoothly without any dropped frames. Taking current gaps on 5G experimental prototypes into account, Ref. [46] proposed a 5G micro-service-based prototype that is able to auto-configure radio resources for network slices with ML. Mainly, the authors focused on eMBB and mMTC types of slices. To orchestrate the slicing mechanism and detect slicing ratio, they used ML-based forecasting. That is, once the training of ML model is completed, their algorithm dynamically provisioned the optimal slicing ratio and created new slices accordingly. As a result, they were able to increase the throughput by approximately 30% compared to the no forecasting case. However, they also pointed out that, this automation comes at the cost of increased utilization of CPU of the host system. Ref. [47] dealt with the prediction and management issues of vehicular use cases in the existing research. Having these issues in mind, they proposed an ML-based resource allocation strategy for vehicular network slicing. Their algorithm works as follows. Firstly, a Convolutional Long Short-Term Memory algorithm is used to analyse temporal and spatial dependencies of service traffic. Then, to keep resource management in accordance with user mobility, a new wireless resource management scheme is used. Finally, the resource allocation algorithm based on the primal dual interior-point method is used to solve the optimal slice weight allocation. Their results showed that the proposed method can successfully predicts the future service traffic and is able to adjust the slice weights accordingly. Ref. [48] also studied vehicle use case in 5G networks. In particular, they focused on electrical vehicles by trying to assign them to charging stations with minimum collision and maximum usage of the network. In order to maintain all the requests, in addition to network slicing, they also utilized an unsupervised ML algorithm. To measure the performance of their algorithm, they conducted several experiments and concluded the that proposed algorithm can allocate resources more efficiently and hence can serve more vehicles compared to a first-come-first-served approach. Similarly, [PERSON]. [49] proposed an intelligent network slicing architecture for vehicular communication services using NFV and ML algorithms.