RES: Real-time Video Stream Analytics using Edge Enhanced Clouds

IEEE With increasing availability and use of Internet of Things (IoT) devices large amounts of streaming data is now being produced at high velocity. Applications which require low latency response such as video surveillance demand a swift and efficient analysis of this data. Existing approaches employ cloud infrastructure to store and perform machine learning based analytics on this data. This centralized approach has limited ability to support analysis of real-time, large-scale streaming data due to network bandwidth and latency constraints between data source and cloud. We propose RealEdgeStream (RES) an edge enhanced stream analytics system for large-scale, high performance data analytics. The proposed approach investigates the problem of video stream analytics by proposing (i) filtration and (ii) identification phases. The filtration phase reduces the amount of data by filtering low value stream objects using configurable rules. The identification phase uses deep learning inference to perform analytics on the streams of interest. The stages are mapped onto available in-transit and cloud resources using a placement algorithm to satisfy the Quality of Service (QoS) constraints identified by a user. The job completion in the proposed system takes 49\% less time and saves 99\% bandwidth compared to a centralized cloud-only based approach