## 3 Integrating Machine Learning into Edge Sliced Networks
### Machine Learning Applied to Network Slicing  
As mentioned in Section 1, network slicing in 5G networks is realized by creating self-contained logical networks consisting of a combination of dedicated and shared resources depending on the needs and demands of each request. In order to efficiently perform construction and management of network slices, a large amount of data generated by mobile device users and vertical industries that are using the emerging services such as automotive communication, AR/VR, and remote healthcare needs to be examined. As it is complex for human beings to handle such high volumes of data in a limited time, one can utilize the machine learning for analysing the data as well as automating the network slicing processes. In particular, the knowledge and patterns we received from ML algorithms can provide us insight on how slices should be created to efficiently and effectively to utilize the edge resources. There are several works in the literature taking advantage of ML during the slicing process. As an example, [PERSON]. [40] developed a novel ML-based scheme for dynamic resource scheduling for networks slicing, aiming to achieve automatic and efficient resource optimization and E2E service reliability. As it is difficult to obtain user data due to privacy concerns, they used RL to extract knowledge by interacting with the network itself. In addition, aiming to improve the feature extraction of the RL framework and automate the decision-making process for resource allocation, the authors used Convolutional Neural Networks. Their experiment results showed that the proposed scheduler outperforms the heuristic, best-effort, and random approaches. [PERSON]. [41] also utilized ML in their study. Considering the fact that data-driven decisions may accelerate the performance of 5G networks, they proposed a Deep Learning Neural Network algorithm that analyses the overall traffic pattern, predicts future traffic, and is able to handle network load. In particular, the three main goals of their study were to effectively create slices, to improve slice creation and allocation processes by predicting traffic, and to be able to respond network failures by adapting the slices. To achieve that, they used random forest algorithm along with deep learning algorithm. Mainly, their algorithm is able to distribute incoming applications to the appropriate slices among eMBB, URLLC, mMTC, or master slices depending on the load and the outputs of their model. As a result, they achieved 95% slice prediction accuracy, i.e., whether the given device is a type of eMBB, URLLC, or mMTC, for unknown devices. Similarly, [PERSON]. [42] designed an RL-based network slicing framework. Mainly, they utilized three network slicing blocks which are a forecasting block that predicts the future traffic based on past information, a slicing admission control block, and a slicing scheduler block to meet the agreed SLAs and report back deviations to the forecasting block. With the help of RL, they are able to learn contemplated traffic models and heterogeneous mobility within slices. As part of this work, isolation between slices, allocation of resources, and the admission of resource requests by network slice tenants are also considered. Their experimental results revealed that the proposed forecasting technique boosts the system utilization up to 100% while reducing the computational time by 20% compared to non-forecasting algorithm. Ref. [43] pointed out that there are still some conflicts on whether to use conventional ML algorithms or to use combination of ML and deep learning for efficient network slicing. To overcome this issue, they compared different techniques and report the performances. Mainly, they proposed an algorithm that combines ML and deep learning which involves three main phases: (1) data collection such as UE type, duration, packet loss ratio, bandwidth, delay rate, speed, jitter, etc.