This paper studies the cost-performance tradeoffs in cloud robotics with heterogeneous cloud service providers, which have complex pricing models and varying application requirements. We present FogROS2-Sky, a cost-efficient open source robotics platform that offloads unmodified ROS2 applications to multiple cloud providers and enables fine-grained cost analysis for ROS2 applications' communication with multiple cloud providers. As each provider offers different options for CPU, GPU, memory, and latency, it can be very difficult for users to decide which to choose. FogROS2-Sky includes an optimization algorithm, which either finds the best available hardware specification that fulfills the user's latency and cost constraints or reports that such a specification does not exist. We use FogROS2-Sky to perform time-cost analysis on three robotics applications: visual SLAM, grasp planning, and motion planning. We are able to sample different hardware setups at nearly half the cost while still create cost and latency functions suitable for the optimizer. We also evaluate the optimizer's efficacy for these applications with the Pareto frontier and show that the optimizer selects efficient hardware configurations to balance cost and latency. Videos and code are available on the website https://sites.google.com/view/fogros2-sky

翻译：本文研究了异构云服务提供商在云机器人中的成本-性能权衡问题，这些提供商具有复杂的定价模型和多样化的应用需求。我们提出了FogROS2-Sky，一个高成本效益的开源机器人平台，该平台可将未修改的ROS2应用卸载到多个云提供商，并支持对ROS2应用与多云提供商通信的细粒度成本分析。由于每个提供商在CPU、GPU、内存和延迟方面提供不同选项，用户往往难以抉择。FogROS2-Sky包含一个优化算法，该算法既可找到满足用户延迟与成本约束的最佳可用硬件规格，也可报告此类规格不存在的情况。我们利用FogROS2-Sky对三种机器人应用（视觉SLAM、抓取规划与运动规划）进行时间-成本分析，能够以近乎半价成本采样不同的硬件配置，同时为优化器生成适配的成本与延迟函数。我们还通过帕累托前沿评估了优化器在这些应用中的有效性，结果表明优化器能够选择高效的硬件配置以平衡成本与延迟。相关视频与代码请访问网站：https://sites.google.com/view/fogros2-sky