O-RAN systems and their deployment in virtualized general-purpose computing
platforms (O-Cloud) constitute a paradigm shift expected to bring unprecedented
performance gains. However, these architectures raise new implementation
challenges and threaten to worsen the already-high energy consumption of mobile
networks. This paper presents first a series of experiments which assess the
O-Cloud's energy costs and their dependency on the servers' hardware, capacity
and data traffic properties which, typically, change over time. Next, it
proposes a compute policy for assigning the base station data loads to O-Cloud
servers in an energy-efficient fashion; and a radio policy that determines at
near-real-time the minimum transmission block size for each user so as to avoid
unnecessary energy costs. The policies balance energy savings with performance,
and ensure that both of them are dispersed fairly across the servers and users,
respectively. To cater for the unknown and time-varying parameters affecting
the policies, we develop a novel online learning framework with fairness
guarantees that apply to the entire operation horizon of the system (long-term
fairness). The policies are evaluated using trace-driven simulations and are
fully implemented in an O-RAN compatible system where we measure the energy
costs and throughput in realistic scenarios.

O-RAN 和 O-Cloud 构成了一个重要的范式转变，可以带来前所未有的性能提升，并提出了一系列试验来评估 O-Cloud 的能耗成本以及其依赖性，同时提出了一种基于能效的计算策略和一种基于射频的策略，以实现在服务器和用户之间公平地平衡能源节约和性能。