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Concordia: teaching the 5G vRAN to share compute

Published: 09 August 2021 Publication History

Abstract

Virtualized Radio Access Network (vRAN) offers a cost-efficient solution for running the 5G RAN as a virtualized network function (VNF) on commodity hardware. The vRAN is more efficient than traditional RANs, as it multiplexes several base station workloads on the same compute hardware. Our measurements show that, whilst this multiplexing provides efficiency gains, more than 50% of the CPU cycles in typical vRAN settings still remain unused. A way to further improve CPU utilization is to collocate the vRAN with general-purpose workloads. However, to maintain performance, vRAN tasks have sub-millisecond latency requirements that have to be met 99.999% of times. We show that this is difficult to achieve with existing systems. We propose Concordia, a userspace deadline scheduling framework for the vRAN on Linux. Concordia builds prediction models using quantile decision trees to predict the worst case execution times of vRAN signal processing tasks. The Concordia scheduler is fast (runs every 20 us) and the prediction models are accurate, enabling the system to reserve a minimum number of cores required for vRAN tasks, leaving the rest for general-purpose workloads. We evaluate Concordia on a commercial-grade reference vRAN platform. We show that it meets the 99.999% reliability requirements and reclaims more than 70% of idle CPU cycles without affecting the RAN performance.

Supplementary Material

shahbaz-public-review (63-public-review.pdf)
Concordia: Teaching the 5G vRAN to Share Compute: Public Review
MP4 File (video-presentation.mp4)
Conference Presentation Video
MP4 File (video-long.mp4)
Long Version Video

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cover image ACM Conferences
SIGCOMM '21: Proceedings of the 2021 ACM SIGCOMM 2021 Conference
August 2021
868 pages
ISBN:9781450383837
DOI:10.1145/3452296
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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New York, NY, United States

Publication History

Published: 09 August 2021

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Author Tags

  1. 5G
  2. NFV
  3. edge computing
  4. machine learning
  5. mobile networks
  6. prediction model
  7. real-time scheduling
  8. vRAN

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  • Research-article

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SIGCOMM '21
Sponsor:
SIGCOMM '21: ACM SIGCOMM 2021 Conference
August 23 - 27, 2021
Virtual Event, USA

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Overall Acceptance Rate 462 of 3,389 submissions, 14%

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  • (2025)Multi-UE 5G RAN Measurements: A Gamut of Architectural OptionsIEEE Access10.1109/ACCESS.2024.352346713(1846-1866)Online publication date: 2025
  • (2024)LuoShenProceedings of the 21st USENIX Symposium on Networked Systems Design and Implementation10.5555/3691825.3691874(877-892)Online publication date: 16-Apr-2024
  • (2024)Democratizing direct-to-cell low earth orbit satellite networksProceedings of the 21st USENIX Symposium on Networked Systems Design and Implementation10.5555/3691825.3691869(791-808)Online publication date: 16-Apr-2024
  • (2024)Assessing the Cloud-RAN in the Linux Kernel: Sharing Computing and Network ResourcesSensors10.3390/s2407236524:7(2365)Online publication date: 8-Apr-2024
  • (2024)Risk-aware continuous control with neural contextual banditsProceedings of the Thirty-Eighth AAAI Conference on Artificial Intelligence and Thirty-Sixth Conference on Innovative Applications of Artificial Intelligence and Fourteenth Symposium on Educational Advances in Artificial Intelligence10.1609/aaai.v38i19.30083(20930-20938)Online publication date: 20-Feb-2024
  • (2024)Fair Resource Allocation in Virtualized O-RAN PlatformsProceedings of the ACM on Measurement and Analysis of Computing Systems10.1145/36390438:1(1-34)Online publication date: 21-Feb-2024
  • (2024)CloudRIC demo: Open Radio Access Network (O-RAN) Virtualization with Shared Heterogeneous ComputingProceedings of the 30th Annual International Conference on Mobile Computing and Networking10.1145/3636534.3698858(1781-1783)Online publication date: 4-Dec-2024
  • (2024)SpotLight: Accurate, Explainable and Efficient Anomaly Detection for Open RANProceedings of the 30th Annual International Conference on Mobile Computing and Networking10.1145/3636534.3649380(923-937)Online publication date: 4-Dec-2024
  • (2024)Unlocking the Non-deterministic Computing Power with Memory-Elastic Multi-Exit Neural NetworksProceedings of the ACM Web Conference 202410.1145/3589334.3645340(2777-2785)Online publication date: 13-May-2024
  • (2024)Aquifer: Transparent Microsecond-scale Scheduling for vRAN WorkloadsIEEE Transactions on Services Computing10.1109/TSC.2024.3440032(1-14)Online publication date: 2024
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