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This paper presents a controlled characterization framework for analyzing the processor-level performance of Centralized Units (CUs) and Distributed Units (DUs) in Open RAN systems. By evaluating these functions under matched hardware and traffic conditions, the study reveals significant differences in CPU time accumulation, with CUs increasing from 17.3 s to 37.4 s and DUs from 462.0 s to 628.4 s during profiling. These insights highlight the need for function-specific optimization strategies to enhance resource provisioning and performance in O-RAN deployments.
Distinct execution characteristics of CUs and DUs reveal that optimizing processor performance in Open RAN could dramatically improve resource efficiency.
Open Radio Access Network (O-RAN) disaggregates RAN protocol functions and enables Centralized Unit (CU) and Distributed Unit (DU) software to execute on general-purpose computing platforms. Different CU and DU protocol responsibilities produce different processor workloads and execution paths. Conventional performance metrics, including CPU utilization and throughput, quantify aggregate resource usage without identifying function-level execution costs or processor microarchitectural bottlenecks. Processor-level characterization, on the other hand, provides insights into resource provisioning, function placement, software optimization, and hardware acceleration. The paper describes a controlled characterization framework that evaluates independently deployed CU and DU functions under matched hardware and traffic conditions. The experimental platform integrates the Linux Foundation OCUDU implementation with an emulated User Equipment, a ZeroMQ-based radio interface, and an Open5GS core. Automated validation confirms registration and bidirectional packet delivery before process-scoped Intel VTune Hotspots and Top-Down Microarchitecture Analysis. Under traffic load, accumulated process CPU time increases from 17.3 s to 37.4 s for the CU and from 462.0 s to 628.4 s for the DU during equal 300-s profiling intervals. The measurements identify distinct CU and DU execution characteristics and motivate function-specific processor analysis and optimization.
