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

2025-08-01

AI Summary

In O-RAN architectures, hardware-software decoupling introduces CPU resource management challenges, as general-purpose OS schedulers fail to jointly optimize real-time performance and energy efficiency for RAN workloads. Method: We propose a lightweight distributed application (dApp) deployed on the Distributed Unit (DU), requiring no kernel modifications or specialized hardware. It enables fine-grained, non-intrusive CPU scheduling via real-time closed-loop control of thread affinity, CPU core isolation, and dynamic voltage and frequency scaling (DVFS), integrating thread-level telemetry (e.g., IPC, cache hit rate, context switches) with OS-level feedback and supporting multi-protocol-stack compatibility. Results: Evaluated on the commercial srsRAN platform, our approach achieves significant power reduction, strict real-time performance guarantees, improved CPU utilization, and negligible system overhead.

Technology Category

• Planning, Routing, and Scheduling: Scheduling
• Machine Learning: Hardware-aware ML
• Search and Optimization: Distributed Search

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments
• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation
• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems

Abstract

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

Problem

• Efficient CPU resource management in O-RAN under real-time constraints
• Reducing energy consumption in RAN workloads with OS schedulers
• Dynamic CPU orchestration without proprietary RAN software or kernel changes

Innovation

• Lightweight dApp for dynamic CPU orchestration
• Thread-level telemetry for real-time adjustments
• O-RAN compliant, no kernel modifications needed