论文标题
不要让我失望！将机器人VFS卸载到云
Don't Let Me Down! Offloading Robot VFs Up to the Cloud
论文作者
论文摘要
机器人服务的最新趋势建议将机器人功能卸载到优势，以满足网络机器人技术的严格延迟要求。但是，边缘通常是一种昂贵的资源，有时云也是一种选择，因此降低了成本。遵循这个想法，我们建议不要让我失望！ （DLMD），一种算法，可在可能的情况下将机器人功能促进卸载机器人功能，以最大程度地减少边缘资源的消耗。此外，DLMD采取适当的迁移，交通转向和无线电交换决策，以满足机器人服务的要求，以作为严格的延迟限制。在本文中，我们制定了DLMD旨在解决，将DLMD性能与先进状态进行比较并进行压力测试以评估小型和大型网络中DLMD性能的优化问题。结果表明，DLMD（i）总是在不到30毫秒的时间内找到解决方案。 （ii）在本地仓库用例中是最佳的，（iii）在网络压力下仅消耗5％的边缘资源。
Recent trends in robotic services propose offloading robot functionalities to the Edge to meet the strict latency requirements of networked robotics. However, the Edge is typically an expensive resource and sometimes the Cloud is also an option, thus, decreasing the cost. Following this idea, we propose Don't Let Me Down! (DLMD), an algorithm that promotes offloading robot functions to the Cloud when possible to minimize the consumption of Edge resources. Additionally, DLMD takes the appropriate migration, traffic steering, and radio handover decisions to meet robotic service requirements as strict latency constraints. In the paper, we formulate the optimization problem that DLMD aims to solve, compare DLMD performance against state of art, and perform stress tests to assess DLMD performance in small & large networks. Results show that DLMD (i) always finds solutions in less than 30ms; (ii) is optimal in a local warehousing use case, and (iii) consumes only 5% of the Edge resources upon network stress.
