FogROS2-Config: Optimizing Latency and Cost for Multi-Cloud Robot Applications
Fuente:
arXiv
Saved in:
| Main Authors: | , , , , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2023
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866913347524362240 |
|---|---|
| author | Chen, Kaiyuan Hari, Kush Khare, Rohil Le, Charlotte Chung, Trinity Drake, Jaimyn Ichnowski, Jeffrey Kubiatowicz, John Goldberg, Ken |
| author_facet | Chen, Kaiyuan Hari, Kush Khare, Rohil Le, Charlotte Chung, Trinity Drake, Jaimyn Ichnowski, Jeffrey Kubiatowicz, John Goldberg, Ken |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2311_05600 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | FogROS2-Config: Optimizing Latency and Cost for Multi-Cloud Robot Applications Chen, Kaiyuan Hari, Kush Khare, Rohil Le, Charlotte Chung, Trinity Drake, Jaimyn Ichnowski, Jeffrey Kubiatowicz, John Goldberg, Ken Robotics Systems and Control 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. |
| title | FogROS2-Config: Optimizing Latency and Cost for Multi-Cloud Robot Applications |
| topic | Robotics Systems and Control |
| url | https://arxiv.org/abs/2311.05600 |