A-Priori Estimation of Computation Times in Fog Networked Robotics

Abstract

Mobile robots and drones have limited onboard computation power, which severely restricts mission planning. With the emergence of Fog Computing, computations may be offloaded to robotic peers, smart gateway devices and remote Cloud virtual machines. In order to effectively make use of such resources, a-priori estimation of execution times of offloaded computational programs is necessary. In this paper, we make use of profiling tools to accurately measure execution times of runtime computations on development testbeds. By exploiting performance benchmarks, we estimate the processing times on heterogeneous robot/Fog/Cloud deployment hardware as well as with varying data sizes. This allows us to determine optimal computational offloading strategies with differing computational complexities, data sizes, parallel processing and heterogeneous devices. We demonstrate our approach on multiple image, video and map processing algorithms deployed using OpenCV. Such design time analysis is crucial for mission planning of autonomous networks of robots and drones.