An Intelligent Zero-Touch Management and Orchestration in 6G: A Green Hierarchical Reinforcement Learning Approach

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.