FogROS2-Config: A Toolkit for Choosing Server Configurations for Cloud Robotics
Kaiyuan Chen, Kush Hari, Rohil Khare, Charlotte Le, Trinity Chung, Jaimyn Drake, Jeffrey Ichnowski, John Kubiatowicz
Abstract
Cloud service providers provide over 50,000 distinct and dynamically changing set of cloud server options. To help roboticists make cost-effective decisions, we present FogROS2-Config, an open toolkit that takes ROS2 nodes as input and automatically runs relevant benchmarks to quickly return a menu of cloud compute services that tradeoff latency and cost. Because it is infeasible to try every hardware configuration, FogROS2-Config quickly samples tests a small set of edge-case servers. We evaluate FogROS2-Config on three robotics application tasks: visual SLAM, grasp planning. and motion planning. FogROS2-Config can reduce the cost by up to 20x. By comparing with a Pareto frontier for cost and latency by running the application task on feasible server configurations, we evaluate cost and latency models and confirm that FogROS2-Config selects efficient hardware configurations to balance cost and latency. Videos and code are available on the website https://sites.google.com/view/fogros2-config
BibTeX
@inproceedings{icra2024_fogros2configato,
title = {FogROS2-Config: A Toolkit for Choosing Server Configurations for Cloud Robotics},
author = {Kaiyuan Chen and Kush Hari and Rohil Khare and Charlotte Le and Trinity Chung and Jaimyn Drake and Jeffrey Ichnowski and John Kubiatowicz and Ken Goldberg},
booktitle = {ICRA 2024},
year = {2024}
}