## 3 Integrating Machine Learning into Edge Sliced Networks
### Machine Learning Applied to Edge Computing  
As mentioned in Section 1, machine learning models are built from the collected data to enable the detection, classification, and prediction of future events. Due to bandwidth, storage, and privacy concerns, it is often impractical to send all the data to a centralized location. As the edge servers have limited storage resources and processing capabilities, using machine learning algorithms can optimize the performance of these servers. In this section, we will present a comprehensive literature review on ML applications in edge computing systems. Utilizing a deep neural network model, Ref. [52] proposed an offloading mechanism for UEs to offload computational intensive tasks to the edge servers, as running such tasks on UEs can limit the potential of MI models and reduce their accuracy. In this respect, they proposed an ML based scheduling and placement algorithm. More specifically, their proposed solution automated the orchestration and deployment of edge applications while guaranteeing efficient usage of resources, scalability, and fault-tolerance of the network. Their experimental results showed a significant gain compared to cloud-based offloading strategies in terms of accuracy and latency. [PERSON] et al. focused on an Industrial IoT case. Different than the existing works that are aimed to improve either power efficiency or the latency of industrial IoT devices, in this work, the authors also focused on improving the service accuracy. Mainly, they used DL-based transfer learning for image recognition tasks and for offloading decisions [53]. As a result, they showed that AI-enabled edge servers could serve more traffic compared to AI-enabled cloud servers. [PERSON]. [54] worked on a vehicular use case. In their work, they focused on how to select which node on which to offload the computationally intensive tasks to make efficient offloading decisions and proposed an ML-based vehicular edge orchestrator. Their algorithm is based on a classifier which detects whether the offloading decisions are successful. In addition, a regression model estimated the service time of all the offloading decisions. Based on these two outcomes, an edge node that has the lowest service time is selected. In this respect, to identify the most successful ML algorithm, they experimented with several ML algorithms such as naive Bayes, support vector machine, etc. One of the key contributions of their work is that they evaluated their algorithm in a realistic setting and proved that their algorithm outperforms random simple moving average based, multi-armed bandit theory based, and game theory based vehicular edge orchestrators in terms of the task failure rate and service times. In their paper, [PERSON]. [55] evaluated the performance of three machine learning algorithms which are k-nearest neighbour, support vector machine, and naive Bayes, running on the edge servers. In particular, they utilized online ML tasks where patterns are extracted from continuous streams of data and the learned models evolved over time to capture complex relationships among many different variables. They stated that, for energy consumption, support vector machine has the largest reward, whereas naive Bayes has the lowest. In fact, their evaluation results are very promising as they are also able to prove the benefits of using ML in the edge nodes on energy consumption. Aiming to provide a scheduling mechanism in real time to realize intelligent cognitive assistant applications, [PERSON]. [56] proposed a novel RL based task assignment approach for the edge servers. As these applications work in real time, especially for healthcare scenarios, they formulated an RL based program to reduce the running time of the assignment task itself. Their simulation results showed that they are able to reduce the task processing time by 13-22% compared to other existing methods. [PERSON]. [57] investigated a joint task, spectrum, and power allocation problem for wireless networks equipped with MEC servers.