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.

翻译：云服务提供商提供超过50,000种各异且动态变化的云服务器配置选项。为协助机器人研究人员制定经济高效的决策，我们提出了FogROS2-Config这一开放工具包。该工具以ROS2节点为输入，自动执行相关基准测试，快速返回一组在延迟与成本间进行权衡的云计算服务菜单。由于逐一测试所有硬件配置不可行，FogROS2-Config通过快速采样少量边缘服务器实例进行测试。我们在三项机器人应用任务（视觉SLAM、抓取规划与运动规划）上评估了FogROS2-Config。实验表明，FogROS2-Config可将成本降低达20倍。通过基于可行服务器配置运行应用任务构建成本与延迟的帕累托前沿，我们评估了成本与延迟模型，并证实FogROS2-Config能够选择高效硬件配置以平衡成本与延迟。