Traffic-Aware Compute Resource Tuning for Energy Efficient Cloud RANs

Pawar, Ujjwal and Tamma, Bheemarjuna Reddy and Antony, Franklin (2021) Traffic-Aware Compute Resource Tuning for Energy Efficient Cloud RANs. In: 2021 IEEE Global Communications Conference, GLOBECOM 2021, 7 December 2021through 11 December 2021, Madrid.

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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. © 2021 IEEE.

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IITH Creators:
IITH CreatorsORCiD
Tamma, Bheemarjuna Reddy
Antony, FranklinUNSPECIFIED
Item Type: Conference or Workshop Item (Paper)
Additional Information: ACKNOWLEDGMENT This work has been supported by the project “CCRAN: Energy Efficiency in Converged Cloud Radio Next Generation Access Network” funded by Intel India.
Uncontrolled Keywords: Cloud-RAN; CPU frequency scaling; Energy Efficiency
Subjects: Computer science
Divisions: Department of Computer Science & Engineering
Depositing User: . LibTrainee 2021
Date Deposited: 06 Oct 2022 11:36
Last Modified: 06 Oct 2022 11:36
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