ALARM：面向高能效vRAN的自适应分层感知资源管理
ALARM: Adaptive Layer-Aware Resource Management for Power-Efficient vRANs
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中文总结 AI 辅助
针对vRAN中gNB整体管理忽视协议层异构性的问题，提出ALARM分层感知资源管理框架，按层监控并针对性扩展CPU资源，实验表明功耗较基线降低33%，较统一方法多降19%。
中文摘要 AI 辅助
向虚拟化无线接入网（vRAN）的过渡使得通过细粒度CPU资源管理实现动态功耗控制成为可能。然而，现有方法将gNB视为一个整体实体，未能利用不同协议层的异构计算特性。本文提出ALARM，一种面向资源受限5G vRAN的分层感知自适应资源管理框架。ALARM将gNB分解为表示具有异构计算需求的不同RAN任务的功能层，并基于处理权重在层级别控制CPU资源。该框架监控每层性能以检测违规情况，并通过仅扩展受影响层来响应。这种针对性调整避免了传统统一扩展方法固有的过度配置问题。在两个资源受限平台上的实验验证表明，与非优化基线相比，功耗降低高达33%，比统一方法多降低19%，动态调整在保持严格实时保证的同时额外节省9.8%的功耗。
英文摘要
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.
发表机构
- CEDRIC Lab, CNAM, Paris, France（巴黎国立高等工艺学院）
机构由 AI 辅助整理，请以论文原文为准。
