FlyNet - a platform to support scientific workflows from the edge to the core for UAV applications.

Many Internet of Things (IoT) applications require compute resources that cannot be provided by the devices themselves. At the same time, processing of the data generated by IoT devices often has to be performed in real- or near real-time. Examples of such scenarios are autonomous vehicles in the form of cars and drones where the processing of observational data (e.g., video feeds) needs to be performed expeditiously to allow for safe operation. To support the computational needs and timeliness requirements of such applications it is essential to include suitable edge resources to execute these applications. In this paper, we present our FlyNet architecture which has the goal to provide a new platform to support workflows that include applications executing at the network edge, at the computing core, and leverage deeply programmable networks. We discuss the challenges associated with provisioning such networking and compute infrastructure on demand, tailored to IoT application workflows. We describe a strategy to leverage the end-to-end integrated infrastructure that covers all points in the spectrum of response latency for application processing. We present our prototype implementation of the architecture and evaluate its performance for the case of drone video analytics workflows with varying computational requirements.