  Open Radio Access Network (Open RAN) has gained tremendous attention from
industry and academia with decentralized baseband functions across multiple
processing units located at different places. However, the ever-expanding scope
of RANs, along with fluctuations in resource utilization across different
locations and timeframes, necessitates the implementation of robust function
management policies to minimize network energy consumption. Most recently
developed strategies neglected the activation time and the required energy for
the server activation process, while this process could offset the potential
energy savings gained from server hibernation. Furthermore, user plane
functions, which can be deployed on edge computing servers to provide
low-latency services, have not been sufficiently considered. In this paper, a
multi-agent deep reinforcement learning (DRL) based function deployment
algorithm, coupled with a heuristic method, has been developed to minimize
energy consumption while fulfilling multiple requests and adhering to latency
and resource constraints. In an 8-MEC network, the DRL-based solution
approaches the performance of the benchmark while offering up to 51% energy
savings compared to existing approaches. In a larger network of 14-MEC, it
maintains a 38% energy-saving advantage and ensures real-time response
capabilities. Furthermore, this paper prototypes an Open RAN testbed to verify
the feasibility of the proposed solution.

Networking and Internet Architecture;Systems and Control;Systems and Control