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