A Comprehensive Modeling and Scheduling Approach for Allocating Distributed Multi-Robot Software to the Edge/Cloud
Yongzhou Zhang, Florian Mirus, Frederik Pasch, Kay-Ulrich Scholl, Christian Wurll, Björn Hein
Abstract
Offloading software modules to the edge/cloud can enhance a robot’s capabilities by leveraging massive computing power. However, determining which software module should be offloaded and scheduled to which robot/edge/cloud node is a challenging task, particularly for robot fleets with diverse tasks. In this paper, we tackle the software scheduling problem and introduce a taxonomy to categorize software modules and classify their applicability and requirements for offloading. Additionally, by using prior measurements, we model the compute cluster and formalize software scheduling as a multi-objective optimization problem which we tackle with a genetic algorithm. To evaluate our approach with a challenging setup, we build a mobile manipulation task using open-source frameworks and libraries in the Robot Operating System (ROS2) community in simulation as well as a mildly simplified real-world variant. Our evaluation shows significant improvements compared to the built-in scheduler of Kubernetes (K8s) regarding robotic specific metrics such as the rate of missed cycle time in both simulated and real-world experiments.
BibTeX
@inproceedings{iros2024_acomprehensivemo,
title = {A Comprehensive Modeling and Scheduling Approach for Allocating Distributed Multi-Robot Software to the Edge/Cloud},
author = {Yongzhou Zhang and Florian Mirus and Frederik Pasch and Kay-Ulrich Scholl and Christian Wurll and Björn Hein},
booktitle = {IROS 2024},
year = {2024}
}