Skip to main content

Cloud-Edge Microservice Architecture for DNN-based Distributed Multimedia Event Processing

  • 380 Accesses

Part of the Communications in Computer and Information Science book series (CCIS,volume 1360)

Abstract

The rise of Big Data, Internet of Multimedia Things (IoMT), and Deep Neural Network (DNN) enabled the growth of DNN-based Computer Vision solutions to Multimedia Event Processing (MEP) applications. When these are applied to a real-world scenario we notice the importance of having a system with a satisfactory speed that can fit in the limited resources of most IoMT devices. However, most solutions for distributed MEP are dependent on a Cloud architecture, which makes these applications migration to the Edge more challenging. As a response to this, we present a microservice architecture for DNN-based distributed MEP over heterogeneous Cloud-Edge environments. We describe our solution that allows for an easier deployment both on the Edge and on the Cloud. We show that choosing the proper tools for an Edge-Friendly solution can lead to 100 times less resource utilisation. Our preliminary investigation shows promising results, with a reduction in energy consumption by 8% with a minor drawback of 15% in throughput in the Edge and a negligible increase in energy consumption on the Cloud.

Keywords

  • Cloud-Independent
  • Edge-Friendly
  • Distributed computing
  • Multimedia Event Processing
  • Deep Neural Networks

This is a preview of subscription content, access via your institution.

Buying options

Chapter
USD   29.95
Price excludes VAT (USA)
  • DOI: 10.1007/978-3-030-71906-7_6
  • Chapter length: 8 pages
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
eBook
USD   64.99
Price excludes VAT (USA)
  • ISBN: 978-3-030-71906-7
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
Softcover Book
USD   84.99
Price excludes VAT (USA)
Fig. 1.
Fig. 2.

Notes

  1. 1.

    COCO dataset: https://cocodataset.org/.

References

  1. Ao, L., Izhikevich, L., Voelker, G.M., Porter, G.: Sprocket: a serverless video processing framework. In: ACM Symposium on Cloud Computing. ACM (2018)

    Google Scholar 

  2. Aslam, A., Curry, E.: Towards a generalized approach for deep neural network based event processing for the internet of multimedia things. IEEE Access6, 25573–25587 (2018)

    Google Scholar 

  3. Belkhir, L., Elmeligi, A.: Assessing ICT global emissions footprint: trends to 2040 & recommendations. J. Cleaner Prod. 177, 448–463 (2018)

    CrossRef  Google Scholar 

  4. Fowler, M., Lewis, J.: Microservices a definition of this new architectural term.http://martinfowler.com/articles/microservices.html (2014)

  5. Fu, X., Ghaffar, T., Davis, J.C., Lee, D.: Edgewise: a better stream processing engine for the edge. In: 2019 USENIX Annual Technical Conference (2019)

    Google Scholar 

  6. Seo, J., Han, S., Lee, S., Kim, H.: Computer vision techniques for construction safety and health monitoring. Adv. Eng. Inform. 29(2), 239–251 (2015)

    CrossRef  Google Scholar 

  7. Strubell, E., Ganesh, A., McCallum, A.: Energy and policy considerations for deep learning in NLP. arXiv preprint arXiv:1906.02243 (2019)

  8. Walker, G., et al.: A practical review of energy saving technology for ageing populations. Appl. Ergon. 62, 247–258 (2017)

    CrossRef  Google Scholar 

  9. Wang, J., et al.: Bandwidth-efficient live video analytics for drones via edge computing. In: 2018 ACM Symposium on Edge Computing, pp. 159–173. IEEE (2018)

    Google Scholar 

  10. Yadav, P., Curry, E.: VidCEP: complex event processing framework to detect spatiotemporal patterns in video streams. In: 2019 IEEE International Conference on Big Data (Big Data), pp. 2513–2522. IEEE (2019)

    Google Scholar 

Download references

Acknowledgement

This work was supported by Science Foundation Ireland under grant SFI/12/RC/2289_P2, co-funded by the European Regional Development Fund.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Felipe Arruda Pontes .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and Permissions

Copyright information

© 2021 Springer Nature Switzerland AG

About this paper

Verify currency and authenticity via CrossMark

Cite this paper

Pontes, F.A., Curry, E. (2021). Cloud-Edge Microservice Architecture for DNN-based Distributed Multimedia Event Processing. In: , et al. Advances in Service-Oriented and Cloud Computing. ESOCC 2020. Communications in Computer and Information Science, vol 1360. Springer, Cham. https://doi.org/10.1007/978-3-030-71906-7_6

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-71906-7_6

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-71905-0

  • Online ISBN: 978-3-030-71906-7

  • eBook Packages: Computer ScienceComputer Science (R0)