  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.
