标题： 能源感知的O-RAN CPU协调：一种dApp驱动的轻量级方法 显示英文标题

标题： Energy-Aware CPU Orchestration in O-RAN: A dApp-Driven Lightweight Approach

摘要： 向软化无线接入网络（RANs）的转变，由开放RAN（O-RAN）范式推动，通过基站功能的解耦和虚拟化实现了灵活的、供应商中立的部署。 然而，这种转变在严格的实时约束下管理CPU资源效率时引入了新的挑战。 特别是，对延迟敏感的RAN工作负载与通用操作系统（OS）调度程序之间的相互作用经常导致次优性能和不必要的能耗。 本研究提出了一种轻量级、可编程的分布式应用（dApp），在分布式单元（DU）级别部署，以动态协调CPU使用。 该dApp与OS形成闭环，利用线程级遥测数据，如上下文切换、每周期指令数（IPC）和缓存指标，实时适应CPU线程亲和性、核心隔离和频率调节。 与现有解决方案不同，它不需要访问专有的RAN软件、硬件特定功能或内核修改。 完全符合O-RAN架构，并且与底层RAN堆栈无关，所提出的解决方案在提高能效和CPU利用率的同时引入了微不足道的开销。 使用商用级srsRAN部署的实验结果表明，在不损害实时处理性能的情况下，实现了持续的节能，突显了低延迟dApp在下一代网络中精细资源控制的潜力。 显示英文摘要

摘要： The transition toward softwarized Radio Access Networks (RANs), driven by the Open RAN (O-RAN) paradigm, enables flexible, vendor-neutral deployments through disaggregation and virtualization of base station functions. However, this shift introduces new challenges in managing CPU resources efficiently under strict real-time constraints. In particular, the interplay between latency-sensitive RAN workloads and general-purpose Operating System (OS) schedulers often leads to sub-optimal performance and unnecessary energy consumption. This work proposes a lightweight, programmable distributed application (dApp) deployed at the Distributed Unit (DU) level to dynamically orchestrate CPU usage. The dApp operates in closed loop with the OS, leveraging thread-level telemetry like context switches, Instructions Per Cycle (IPC), and cache metrics, to adapt CPU thread affinity, core isolation, and frequency scaling in real time. Unlike existing solutions, it requires no access to proprietary RAN software, hardware-specific features, or kernel modifications. Fully compliant with the O-RAN architecture and agnostic to the underlying RAN stack, the proposed solution introduces negligible overhead while improving energy efficiency and CPU utilization. Experimental results using a commercial-grade srsRAN deployment demonstrate consistent power savings without compromising real-time processing performance, highlighting the potential of low-latency dApps for fine-grained resource control in next-generation networks