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YinYangRAN: Resource Multiplexing in GPU-Accelerated Virtualized RANs

RAN virtualization is revolutionizing the telco in- dustry, enabling 5G Distributed Units to run using general- purpose platforms equipped with Hardware Accelerators (HAs).

Information

RAN virtualization is revolutionizing the telco in- dustry, enabling 5G Distributed Units to run using general- purpose platforms equipped with Hardware Accelerators (HAs). Recently, GPUs have been proposed as HAs, hinging on their unique capability to execute 5G PHY operations efficiently while also processing Machine Learning (ML) workloads.
While this ambivalence makes GPUs attractive for cost-effective deploy- ments, we experimentally demonstrate that multiplexing 5G and ML workloads in GPUs is in fact challenging, and that using conventional GPU-sharing methods can severely disrupt 5G operations. We then introduce YinYangRAN, an innovative O- RAN-compliant solution that supervises GPU-based HAs so as to ensure reliability in the 5G processing pipeline while maximizing the throughput of concurrent ML services.

https://jaayala.github.io/papers/24_infocom_2.pdf

Author/Speaker/Contributor

Leonardo Lo Schiavo, Jose A. Ayala-Romero, Andres Garcia-Saavedra, Marco Fiore, Xavier Costa-Perez, Universidad Carlos III de Madrid, Spain, IMDEA Networks Institute, Spain, NEC Laboratories Europe GmbH, Germany, i2CAT Foundation and ICREA, Spain

Event/Publication

IEEE

Date

May 2024