Power-efficient 5G networks save energy by managing layers separately
ALARM: Adaptive Layer-Aware Resource Management for Power-Efficient vRANs
Networking and Internet Architecture
Summary
Mobile networks use different types of work inside their systems, but usually treat all parts the same when managing computer power, which wastes energy. The authors made ALARM, a system that watches each part of the network separately and adjusts computing power only where needed. This saves electricity while still keeping the network fast and reliable. Tests showed ALARM reduces power use significantly compared to older methods.
What this means in practice
- •For 5g network operators: Improve energy efficiency of 5G base stations by dynamically allocating CPU resources to specific network layers under constrained hardware.
- •For telecommunications infrastructure engineers: Design energy-saving resource control schemes for virtualized network functions by targeting computational layers individually rather than the entire system.
Authors
Ali Srour, Farzad Veisi, Sami Taktak, Vania Conan
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.