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.

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.