Traffic-Aware Compute Resource Tuning for Energy Efficient Cloud RANs
Traffic-Aware Compute Resource Tuning for Energy Efficient Cloud RANs
复制标题
针对节能云 RAN 的流量感知计算资源调整
DOI：
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发表时间：
2021
期刊：
影响因子：
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通讯作者：
Franklin A Antony
中科院分区：
文献类型：
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作者：
Ujjwal Pawar;B. R. Tamma;Franklin A Antony
云无线电接入网络（C-RAN）以这样的方式分解基站的功能：一些无线电处理任务集中在云平台上的通用处理器（GPP）的虚拟化计算机池中。这使得能够基于小区站点处的时空业务波动来有效利用计算资源。在本文中，我们试图通过C-RAN在云平台上进一步减少计算资源。首先，我们分析了使用现有的Linux CPU频率缩放调节器的基于OpenAirInterface（OAI）的C-RAN系统中消耗的能量。基于观察，我们提出了一个交通感知的计算资源调整（CRT）计划，减少C-RAN的能源消耗。CRT方案通过在非高峰时间期间的每个调度间隔中利用所有可用的无线电资源来机会性地降低在服务用户时使用的调制编码方案（MCS）。MCS的这种减少有助于降低能耗（由于在云平台的GPP中使用较低的CPU时钟频率）和前传带宽要求。CRT方案的另一个好处是它能够与任何MAC调度器一起工作。大量的仿真结果表明，CRT如何优于现有的频率缩放总督在能源消耗，同时减少前传带宽的要求。
Cloud Radio Access Network (C-RAN) disaggregates the functionalities of the base station in a way that some of the radio processing tasks are centralized in a virtualized computer pool of general-purpose processors (GPPs) on a cloud platform. This enables efficient utilization of the computational resources based on the spatio-temporal traffic fluctuations at cell sites. In this paper, we attempt to further reduce the computation resources by C-RAN on the cloud platform. First, we profiled the energy consumed in an OpenAirInterface (OAI) based C-RAN system using the existing Linux CPU frequency scaling governors. Based on the observations, we propose a traffic-aware compute resource tuning (CRT) scheme that reduces the energy consumption of C-RANs. The CRT scheme opportunistically lowers Modulation Coding Scheme (MCS) used while serving users by utilizing all of the available radio resources in every scheduling interval during non-peak hours. This reduction in the MCS helps in reducing energy consumption (due to usage of lower CPU clock frequency in the GPPs of the cloud platform) and fronthaul bandwidth requirements. Another benefit of the CRT scheme is its ability to work with any MAC scheduler. The extensive simulation results show how the CRT outperforms the existing frequency scaling governors in energy consumption while reducing fronthaul bandwidth requirements.