Scheduling DNN Inferencing on Edge and Cloud for Personalized UAV Fleets

Abstract

Drone fleets with onboard cameras coupled with DNN inferencing models can support diverse applications, from infrastructure monitoring to package deliveries. Here, we propose to use one or more “buddy” drones to help Visually Impaired People (VIPs) lead an active lifestyle. Video inferencing tasks from such drones are used to navigate the drone and alert the VIP to threats, and hence have strict execution deadlines. They have a choice to execute either on an accelerated edge like Nvidia Jetson linked to the drone, or on a cloud INFerencing-as-a-Service (INFaaS). However, making this decision is a challenge given the latency and cost trade-offs, and network variability in outdoor environments. We propose a deadline-driven heuristic to schedule a stream of diverse DNN inferencing tasks executing over video segments generated by multiple drones linked to an edge, with the option to execute on the cloud. We use strategies like task dropping, work stealing and migration, and dynamic adaptation to cloud variability, to fully utilize the captive edge with intelligent offloading to the cloud, to maximize the utility and the number of tasks completed. We evaluate our strategies using a setup that emulates a fleet of > 50 drones within city conditions supporting> 25 VIPs, with real DNN models executing on drone video streams, using Jetson Nano edges and AWS Lambda cloud functions. Our detailed comparison of our strategy exhibits a task completion rate of up to 91 %, up to 2.5× higher utility compared to the baselines and 68% higher utility with network variability.