Towards Practical Cloud Offloading for Low-cost Ground Vehicle Workloads
Towards Practical Cloud Offloading for Low-cost Ground Vehicle Workloads
复制标题
实现低成本地面车辆工作负载的实用云卸载
DOI：
10.1109/ipdps49936.2021.00083
复制
发表时间：
2021-05
期刊：
影响因子：
--
通讯作者：
Yungang Bao
中科院分区：
文献类型：
--
作者：
Yuan Xu;Tianwei Zhang;Jimin Han;Sa Wang;Yungang Bao
低成本地面车辆(LGV)已被广泛用于执行日常生活中的各种任务。然而，有限的车载电池容量和计算资源阻碍了LGV承担更复杂和智能的工作负载。一种很有前途的方法是将计算从本地LGV卸载到远程服务器。然而，目前云机器人的研究和平台仍处于非常早期的阶段。与其他系统和设备相比，优化LGV负载分流面临着更多的挑战，如环境的不确定性和设备的移动性，本文从性能、能效和网络健壮性的角度探讨了优化LGV工作负载云分流的机会。我们首先建立一个分析模型来揭示LGV工作负载中每个函数的计算角色和影响。然后提出了几种优化策略(细粒度迁移、云加速、实时监控和调整)来加速工作量计算，降低车载能耗，增强网络的健壮性。我们实现了一个端到端的云机器人框架，并采用这样的策略来实现动态和自适应的卸载。在物理LGV上的评估表明，该策略可以显著降低总能耗2.12倍，任务完成时间2.53倍，并在网络质量较差的情况下保持较强的健壮性。
Low-cost Ground Vehicles (LGVs) have been widely adopted to conduct various tasks in our daily life. However, the limited on-board battery capacity and computation resources prevent LGVs from taking more complex and intelligent workloads. A promising approach is to offload the computation from local LGVs to remote servers. However, current cloud-robotic research and platforms are still at a very early stage. Compared to other systems and devices, optimizing LGV workload offloading faces more challenges, such as the uncertainty of environments and the mobility feature of devices.In this paper, we explore the opportunities of optimizing cloud offloading of LGV workloads from the perspectives of performance, energy efficiency and network robustness. We first build an analytical model to reveal the computation role and impact of each function in LGV workloads. Then we propose several optimization strategies (fine-grained migration, cloud acceleration, real-time monitoring and adjustment) to accelerate workload computation, reduce on-board energy consumption, and increase the network robustness. We implement an end-to-end cloud-robotic framework with such strategies to achieve dynamic and adaptive offloading. Evaluations on physical LGVs show that our strategies can significantly reduce the total energy consumption by 2.12× and mission completion time by 2.53×, and maintain strong robust ness under poor network quality.
登录
查看更多内容
影响因子：
22.7
作者：
Thrun, S
通讯作者：
Thrun, S
DOI：
--
发表时间：
2015-05
期刊：
--
影响因子：
--
作者：
通讯作者：
--
DOI：
--
发表时间：
1967
期刊：
--
影响因子：
--
作者：
J. E. Falk;R Fletcher;M. J. D. Powell;Peter E Hart;Nils J Nilsson;Bertram Raphael
通讯作者：
J. E. Falk;R Fletcher;M. J. D. Powell;Peter E Hart;Nils J Nilsson;Bertram Raphael
DOI：
10.1145/1966445.1966473
发表时间：
2011-04
期刊：
--
影响因子：
--
作者：
Byung-Gon Chun;Sunghwan Ihm;Petros Maniatis;M. Naik;A. Patti
通讯作者：
Byung-Gon Chun;Sunghwan Ihm;Petros Maniatis;M. Naik;A. Patti
DOI：
10.1145/1134680.1134705
发表时间：
2006-06
期刊：
--
影响因子：
--
作者：
Anthony J. Nicholson;Y. Chawathe;Mike Y. Chen;Brian D. Noble;D. Wetherall
通讯作者：
Anthony J. Nicholson;Y. Chawathe;Mike Y. Chen;Brian D. Noble;D. Wetherall