商业O-RAN系统的能耗测试与建模
Energy Efficiency Testing and Modeling of a Commercial O-RAN System
AI总结：
本文通过实测分析，对商用O-RAN系统进行能耗建模，揭示影响能效的关键因素，为运营商优化网络能耗提供数据支持。
摘要：
网络能效对于移动网络运营商来说，在经济和生态方面都至关重要。O-RAN架构的出现带来了解耦和虚拟化，为了实现最大的节能收益，我们需要在组件和系统层面进行严格测量、分析和建模能耗。然而，仍然缺乏公开可用的、定量数据来表征商用O-RAN系统的性能。在本文白皮书中，我们基于全面的功率和性能测量，展示了一个商业O-RAN系统的详细能效表征和建模，使用了一个忠实复现由无线运营商部署的生产O-RAN网络的网络部署。结果来自通过Open RAN集成与部署（ORCID）实验室测试与评估（T&E）项目和Open Networking Foundation / Rutgers WINLAB能效研发项目联合开展的能耗测试活动。测试环境包括一个带有AWS托管O-CU、专用服务器O-DU和六个高功率、多频段O-RU的O-RAN系统。我们的结果识别了影响O-RAN堆栈中功率消耗的主要因素，并量化了不同操作和流量场景下的能耗变化。这些测量可以用于运营商参数化功率消耗模型，最终支持数据驱动的能效优化和更可持续的商用O-RAN网络运营。
英文摘要：
Network energy efficiency is of critical importance to mobile network operators for economic and ecological reasons. The advent of the O-RAN architecture has brought disaggregation and virtualization, and in order to achieve the highest energy savings gains, we need rigorous measurement, analysis, and modeling of energy consumption at both the component and system levels. However, there remains a lack of publicly-available, quantitative data characterizing the behavior of commercial-grade O-RAN systems. In this white paper, we present a detailed energy-efficiency characterization and modeling of a commercial O-RAN system based on comprehensive power and performance measurements, using a network deployment that faithfully replicates a production O-RAN network deployed by a wireless carrier. The results are drawn from an energy test campaign conducted through a joint collaboration between the Open RAN Center for Integration and Deployment (ORCID) Lab Testing and Evaluation (T&E) Project and the Open Networking Foundation / Rutgers WINLAB Energy Efficiency R&D project. The test environment includes an O-RAN system with an AWS-hosted O-CU, a dedicated-server O-DU, and six high-power, multi-band O-RUs. Our results identify the dominant factors influencing power consumption across the O-RAN stack and quantify energy usage variation under different operational and traffic scenarios. These measurements can be used by operators to parameterize power-consumption models, ultimately supporting data-driven energy optimization and more sustainable operation of commercial O-RAN networks.
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