🤖 AI Summary
本文提出ALARM框架，通过分层管理和动态调整5G虚拟化无线接入网中的CPU资源，解决现有方法未考虑协议层异构计算特性的问题，实现节能。
📝 Abstract
The transition to virtualized Radio Access Networks (vRAN) enables dynamic power control through fine-grained CPU resource management. However, existing approaches treat gNB as a monolithic entity, failing to exploit the heterogeneous computational characteristics of different protocol layers. This paper proposes ALARM, a layer-aware adaptive resource management framework for constrained 5G vRAN. ALARM decomposes the gNB into functional layers representing distinct RAN tasks with heterogeneous computational demands, and controls CPU resources at the layer level based on processing weights. The framework monitors per-layer performance to detect violations and responds by scaling only the affected layer. This targeted adaptation avoids the over-provisioning inherent in traditional uniform scaling approaches. Experimental validation on two constrained platforms demonstrates up to 33% power reduction versus non-optimized baseline and 19% beyond uniform approaches, with dynamic adaptation achieving 9.8% additional savings while preserving strict real-time guarantees.