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Many robotic applications that are critical for robot performance requireimmediate feedback, hence execution time is a critical concern. Furthermore, itis common that robots come with a fixed quantity of hardware resources; if anapplication requires more computational resources than the robot canaccommodate, its onboard execution might be extended to a degree that degradesthe robot performance. Cloud computing, on the other hand, features on-demandcomputational resources; by enabling robots to leverage those resources,application execution time can be reduced. The key to enabling robot use ofcloud computing is designing an efficient offloading algorithm that makesoptimum use of the robot onboard capabilities and also forms a quick consensuson when to offload without any prior knowledge or information about theapplication. In this paper, we propose a predictive algorithm to anticipate thetime needed to execute an application for a given application data input sizewith the help of a small number of previous observations. To validate thealgorithm, we train it on the previous N observations, which includeindependent (input data size) and dependent (execution time) variables. Tounderstand how algorithm performance varies in terms of prediction accuracy anderror, we tested various N values using linear regression and a mobile robotpath planning application. From our experiments and analysis, we determined thealgorithm to have acceptable error and prediction accuracy when N>40.

A Predictive Application Offloading Algorithm Using Small Datasets for Cloud Robotics