Incident-Supporting Visual Cloud Computing Utilizing Software-Defined Networking in GENI

In the event of natural or man-made disasters, providing rapid situational awareness through video/image data collected at salient incident scenes is often critical to first responders. However, computer vision techniques that can process the media-rich and data-intensive content obtained from civilian smartphones or surveillance cameras require large amounts of computational resources or ancillary data sources that may not be available at the geographical location of the incident. In this paper, we evaluate proposed in our earlier works the incidentsupporting visual cloud computing (VCC) architecture (which supports computing at the network-edges and software-defined networking (SDN)) that can enhance public safety application performance to near real-time in comparison with common core cloud computing over IP networks in real settings . To this end, we design and develop a set of reproducible experiments in NSF Global Environment for Network Innovations (GENI) platform by first developing the VCC SDN controller and then creating GENI Custom Images of both developed SDN controller and the public safety application, as well as all corresponding GENI Resource Specifications (RSpecs) and bash scripts to enhance experiment reproducibility.