## 3 Integrating Machine Learning into Edge Sliced Networks
### Machine Learning Applied to Edge-Enable Network Slicing  
As the emerging 5G services have highly diverse performance requirements such as high bandwidth, low latency, high reliability, etc., efficient accommodation of those needs requires the use of edge-enabled slicing together with ML techniques. In this respect, there are a couple of studies in the literature that employ ML algorithms in edge-enabled slicing frameworks. As an example, [PERSON]. [60] proposed a resource orchestration method for wireless edge networks based on decentralized deep RL and network slicing techniques. In particular, the deep RL agent learns the resource demands of network slices and orchestrates the resource allocation accordingly. The authors stated that, due to the temporal and spatial dynamics of the slice traffic and the complex tradeoffs between the performance of network slices and the resource orchestration, it is inefficient to use a centralized learning agent to orchestrate resource allocations to network slices. Additionally, a centralized learning agent needs to obtain network performance data from all the network nodes, which introduces excessive communication overhead and delay. Thus, they adopted a decentralized deep RL approach to automate dynamic E2E network slicing and to optimize the performance of the slices under the constrained networking and computing resources. Simulation results showed that their orchestration agent is able to autonomously orchestrate E2E resources under varying slice traffic. Considering data-intensive and latency-sensitive tasks, [PERSON]. [61] proposed to extend network slices to aerial vehicles equipped with MEC nodes. This is because when data are produced by ground devices they may not able to reach the edge servers in time. Particularly, this model consisted of a RL agent that analyses the environment and decides whether to offload jobs to aerial vehicles based on power consumption, job loss, and incurred delay. Experimental results showed that this proposed method can work at runtime with a great flexibility. Combined with edge computing and network slicing, ML based solutions enable autonomous slice management, control, orchestration, and optimization. In this respect, one of the main advantages of ML is to accurately classify types of applications for automating the network slicing process. In addition to that, ML models can also be useful for optimizing resource allocation in edge enabled network slicing, particularly for predicting, assigning, and optimizing the limited edge resources. An overview of previously mentioned ML assisted studies is presented in Table 3.  
It should be also noted that, in most of the systems with network slicing mechanisms, all the useful information gathered and analysed by ML algorithms are conveyed to network slice controllers which then manage slice life-cycles and configure necessary resources and functions. Due to the complexity of these tasks, a network slice controller can be composed of multiple orchestrators that are in charge of a subset of functionalities. Thus, to be able to ensure and maximize the benefits received from these solutions, it is important to ensure that the controllers are in coordination with each other about the status of the operations and maintaining a centralized view.