A Generic Framework for Deploying Video Analytic Services on the Edge

This article introduces a novel distributed model for handling in real-time, edge-based Artificial Intelligence analytics, such as the ones required for smart video surveillance. The novelty of the model relies on decoupling and distributing the services into several decomposed functions which are linked together, creating virtual function chains (VFCVFC model). The model considers both computational and communication constraints. Theoretical, simulation and experimental results have shown that the VFCVFC model can enable the support of heavy-load services to an edge environment while improving the footprint of the service compared to state-of-the art frameworks. In detail, results on the VFCVFC model have shown that it can reduce the total edge cost, compared with a Monolithic and a Simple Frame Distribution models.