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.

翻译：O-RAN系统及其在虚拟化通用计算平台（O-Cloud）中的部署构成了一种范式转变，预计将带来前所未有的性能提升。然而，这些架构引发了新的实现挑战，并可能加剧移动网络本已高企的能源消耗。本文首先通过一系列实验评估O-Cloud的能耗成本及其对服务器硬件、容量和数据流量特性（通常随时间变化）的依赖性。随后，提出一种以能效方式将基站数据负载分配给O-Cloud服务器的计算策略，以及一种近实时确定每个用户最小传输块大小以避免不必要能耗的无线策略。这些策略在节能与性能之间取得平衡，并确保两者分别在服务器和用户之间公平分配。为应对影响策略的未知且时变参数，我们开发了一种新颖的在线学习框架，该框架具有适用于系统整个运行周期的公平性保障（长期公平性）。通过基于真实数据驱动的仿真评估这些策略，并在兼容O-RAN的系统中完整实现，测量实际场景中的能耗成本与吞吐量。