Mean-Field Multi-Agent Contextual Bandit for Energy-Efficient Resource Allocation in vRANs
In this paper, we gather data from our experimental platform and compare the performance and energy consumption of a HA (NVIDIA GPU V100) vs. a CPU (Intel Xeon Gold 6240R, 16 cores) for energy-friendly software processing.
Information
Radio Access Network (RAN) virtualization, key for new-generation mobile networks, requires Hardware Accelerators (HAs) that swiftly process wireless signals from Base Stations (BSs) to meet stringent reliability targets. However, HAs are expensive and energy-hungry, which increases costs and has serious environmental implications. To address this problem, we gather data from our experimental platform and compare the performance and energy consumption of a HA (NVIDIA GPU V100) vs. a CPU (Intel Xeon Gold 6240R, 16 cores) for energy-friendly software processing.
Author/Speaker/Contributor
Jose A. Ayala-Romero, Leonardo Lo Schiavo, Andres Garcia-Saavedra, Xavier Costa-Perez
Event/Publication
IEEE INFOCOM 2024 - IEEE Conference on Computer Communications
Date
August 2024
