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Hybrid Resource Scheduling Scheme for Video Surveillance in GPU-FPGA Accelerated Edge Computing System

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Advances in Artificial Intelligence and Applied Cognitive Computing

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Abstract

Video surveillance system with object re-identification is cited as a challenge to address to enhance the safety and convenience of citizens. The system consists of a combination of complex tasks requiring a lot of computing workload. With these characteristics, efforts have continued to accelerate the system. Existing systems did not benefit from the service latency perspective to make good use of heterogeneous accelerated edge computing system. In this paper, the goal is to accelerate the system used in smart cities on limited heterogeneous edge servers, and the scheduling planning method considering them is proposed. We first identify the computational volume-based execution time model of the heterogeneous accelerators. Then, we propose a scheduling plan that distributes this task graph to resources. Finally, the planning method proposed in this paper is experimentally compared with the previous GPU-FPGA allocation scheme. We compare it to the previously proposed method, and show that queue latency can be reduced, with showing robustness to the deadline violation rate.

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Acknowledgements

This work was partly supported by “The Cross-Ministry Giga KOREA Project” grant funded by the Korea government(MSIT) (No.GK20P0400, Development of Mobile-Edge Computing Platform Technology for URLLC Services) and in part by Samsung Electronics, Device Solution (DS).

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Authors and Affiliations

  1. School of Electrical Engineering, Korea Advanced Institute of Science and Technology, Daejeon, South Korea

    Gyusang Cho, Seong-Hwan Kim & Chan-Hyun Youn

Authors
  1. Gyusang Cho
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  2. Seong-Hwan Kim
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  3. Chan-Hyun Youn
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Corresponding author

Correspondence to Chan-Hyun Youn .

Editor information

Editors and Affiliations

  1. Department of Computer Science, University of Georgia, Athens, GA, USA

    Hamid R. Arabnia

  2. Department of Electrical and Computer Engineering, University of Manitoba, Winnipeg, MB, Canada

    Ken Ferens

  3. Business Administration, University of Oviedo, Oviedo, Asturias, Spain

    David de la Fuente

  4. Institute of Informatics Problems, The Russian Academy of Sciences, Moscow, Russia

    Elena B. Kozerenko

  5. Technology and Information systems, Universidad de Castilla La Mancha, Ciudad Real, Ciudad Real, Spain

    José Angel Olivas Varela

  6. Facultad de Informática - CIC PBA, Universidad Nacional de La Plata, La Plata, Argentina

    Fernando G. Tinetti

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Cho, G., Kim, SH., Youn, CH. (2021). Hybrid Resource Scheduling Scheme for Video Surveillance in GPU-FPGA Accelerated Edge Computing System. In: Arabnia, H.R., Ferens, K., de la Fuente, D., Kozerenko, E.B., Olivas Varela, J.A., Tinetti, F.G. (eds) Advances in Artificial Intelligence and Applied Cognitive Computing. Transactions on Computational Science and Computational Intelligence. Springer, Cham. https://doi.org/10.1007/978-3-030-70296-0_49

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  • DOI: https://doi.org/10.1007/978-3-030-70296-0_49

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-70295-3

  • Online ISBN: 978-3-030-70296-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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