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

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


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.


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

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  • DOI: 10.1007/978-3-030-71906-7_6
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This work was supported by Science Foundation Ireland under grant SFI/12/RC/2289_P2, co-funded by the European Regional Development Fund.

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Correspondence to Felipe Arruda Pontes .

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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.

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  • Print ISBN: 978-3-030-71905-0

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