# On End-to-End Intelligent Automation of 6G Networks
## 4 6G End-to-End Network Automation Challenges
### AI-Driven Orchestration  
The fourth challenge facing 6G network automation and orchestration is how to leverage AI and ML technologies and for what tasks. This is particularly important given the large volume and heterogeneous nature of data that is expected to be generated by the core, edge, and specifically extreme edge devices. Although AI and ML technologies are being considered to manage and orchestrate current 5G network deployments [84; 85], 6G requires that such technologies are used in a much more comprehensive manner that takes into account the architectural advancement of 6G networks. One of the challenging aspects of incorporating AI/ML technologies into 6G network orchestration lies in determining what orchestration operations are being targeted. For example, AI/ML techniques can be used to predict required network scaling actions to ensure that traffic demands are met. Alternatively, these techniques can be used to take proactive network function placement decisions (whether at the core, edge, or extreme edge) based on the available resources as well as the requirements of the services formed by these functions. Moreover, they can be used to provide a proactive alerting system that interfaces with other systems and functions. Therefore, deciding on what orchestration operations need to be driven by these AI/ML technologies and where to place the corresponding module is a key issue to consider as part of the efforts of having AI-driven orchestration. Another crucial and also challenging aspect is achieving data-driven cognitive management and orchestration--more specifically, the concept of closed-loop automation (CLA) that goes beyond current network analytics towards full cognitive and automated orchestration in the entire system. To achieve this, two main sub-challenges need to be considered. The first is the need for high-precision fine-grained data that can facilitate accurate decision-making. Thus, it is crucial to develop smart data collection mechanisms that only gather the data needed for proper analytics and monitoring purposes. The second sub-challenge is facilitating cooperative and closed-loop mechanisms. This is particularly important given the fact that orchestration operations span across multiple domains and slices. Hence, any developed CLA mechanism needs to allow both the horizontal (across domains) and vertical (within a domain) interaction of closed loops as part of any data-driven approach. A third essential challenging aspect that will also rely on data-driven approaches is intent-based orchestration. Intent can be defined as \"the formal specification of all expectations including requirements, goals, and constraints given to a technical system\" [99]. Accordingly, there is a differentiation between intent and the corresponding implementation logic of this intent. Therefore, to offer complete intent-based management and orchestration, multiple data-driven components that rely on AI and ML algorithms are needed. This includes components such as the intent interface, state modeling and anomaly detection, and self-learning closed loops, among others. Moreover, these components need to also facilitate cross-layer prediction and interaction across the different layers (e.g., infrastructure layer, management layer, data plane layer, etc.). This is important to ensure that these mechanisms are able to process and correlate the collected heterogeneous data. Finally, an important aspect to consider when adopting and developing AI-driven orchestration mechanisms is explainability. Explainability is defined as making the AI systems'/agents' learning and decision-making process traceable and understandable for humans [100; 101]. This is particularly important given that the decisions made by these learning agents have a direct impact on humans and, as such, humans should be able to trust and be convinced that these decisions are the correct ones.