Paper published in 1st International Workshop on Edge Migration and Architecture (EdgeWays 2020)
Felipe Arruda Pontes, Edward Curry, “Cloud-Edge Microservice Architecture for DNN-based Distributed Multimedia Event Processing”, In 1st International Workshop on Edge Migration and Architecture (EdgeWays 2020), 2020.
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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.