Fully autonomous, zero-touch systems emphasizing on energy efficiency, high reliability, and ultra-low latency will be possible with the introduction of 6G networks. But with more devices and services, energy usage is expected to rise, necessitating sustainable solutions. A Decision Engine (DE) based on Hierarchical Reinforcement Learning (HRL) is presented in this research to improve the deployment of Service Function Chains (SFCs) based on Cloud-Native Functions (CNF) in dynamic contexts. The goal of the framework is to lower energy consumption while improving scalability and flexibility in the cloud, far-edge, and edge domains. By simulating actual 6G situations, we demonstrate that the HRL-based DE improves resource allocation, reduces latency by 80%, and considerably reduces energy usage by 60% compared to the flat RL. By assisting in self-optimizing network management, our method presents a viable route to intelligent, sustainable 6G networks.

As commercial 5G networks become commercially available and 6G looms in the horizon, the adoption of this new technologies depends on the way current and yet-to-come vertical industries put them to use. This accelerates the process of adoption by the end users, which are the final consumers of these technologies. 5G networks have multiple use cases associated with its new features, being the Ultra-Reliable Low Latency Communications (URLLC) use case one of the most instrumental for verticals such as remote teleoperation, factory automation and autonomous driving. In this paper, we design a general purpose and low-cost end-to-end (E2E) 5G/Beyond-5G Experimental Platform for URLLC applications, supporting Platform-as-a-Service (PaaS) with Artificial Intelligence (AI) and Machine Learning (ML) capabilities for online data analytics and automated decision making. The experimental evaluation of our platform demonstrates an average one-way latency on the DL and UL as low as 4.1 ms and 6.60 ms, respectively, 13.8 ms of end-to-end latency (E2EL), and a 31.7 ms E2EL between UEs for data frames of a teleoperation Web App with video feedback, demonstrating the capability of our platform in relation to other state-of-the-art testbeds for URLLC applications.