## 3 Integrating Machine Learning into Edge Sliced Networks
### Machine Learning Applied to Network Slicing
Mainly, their algorithm composed of four layers which are a network infrastructure virtualization layer, which virtualizes resources from a dedicated hardware; an intelligent control layer, which determines patterns of vehicular networks and performs self-configuration of network slices; a network slice layer, which defines different slices based on QoS requirements; and a service layer which captures the QoS requirements. In this work, they used Deep RL in combination with convolutional networks as well as a Long Short-Term Memory algorithm. This is because their data is stochastic but also has many dimensions. They concluded that the proposed framework is promising as it can improve QoS by allowing the control layer to make decisions according to historical observations. Focusing on the Industrial IoT application, Ref. [50] proposed a dynamic slicing method to guarantee QoS by isolating urgent traffic and eliminating resource starvation. To do that, they utilized an online learning method that clusters devices to the most appropriate slices using Online Gaussian Mixture Model. Their simulation results highlighted the efficiency of the proposed method in avoiding resources starvation and providing QoS for devices while respecting the latency requirements and decreasing the energy consumption. Different to the above studies, Ref. [51] presented a fairly new approach. In particular, to optimize load balancing further, they designed an ML-based network sub-slicing model, where each logical slice is also divided into slices, called as sub-slices. Considering the fact that each network slice needs to fulfil several different requirements, such as low latency, high reliability, and high spectral efficiency, their aim was to create sub-slices where each sub-slice focuses on a single requirement. As an example, one sub-slice focuses on spectral efficiency, whereas the other focuses on providing low latency with reduced power consumption. In particular, to identify requirements of different applications, they utilized a Support Vector Machine algorithm. In addition, to create clusters of sub-slices for grouping similar types of application services, they used K-means algorithm. Experimental results showed that the proposed algorithm outperforms the state-of-the-art algorithms in terms of improved performance and reduced energy consumption. Considering the above applications, it can be seen that ML is a powerful and commonly used tool for network slicing as it yields improved performance and faster convergence in network management automation. That is, due to the complexity of the performed tasks which address different purposes, network slice controllers, being responsible for the monitoring and management of the functionalities of the overall system, can use the feedback and results obtained from ML algorithms. In particular, for network resource management in large-scale systems, ML-based slicing methods can be used by network controllers to solve the joint allocation problem of communication, caching, and computing resources due to its ability to handle complicated, dynamic, and heterogeneous features.