Optimal Algorithm Allocation for Single Robot Cloud Systems
This addresses cost reduction for robot manufacturers or users by enabling cheaper robots through cloud offloading, but it is incremental as it builds on existing cloud robotics concepts.
The paper tackles the problem of optimizing algorithm allocation between a robot and cloud infrastructure to reduce robot cost while maintaining performance, providing a general model and simulation results.
In order for a robot to perform a task, several algorithms need to be executed, sometimes, simultaneously. Algorithms can be run either on the robot itself or, upon request, be performed on cloud infrastructure. The term cloud infrastructure is used to describe hardware, storage, abstracted resources, and network resources related to cloud computing. Depending on the decisions on where to execute the algorithms, the overall execution time and necessary memory space for the robot will change accordingly. The price of a robot depends, among other things, on its memory capacity and computational power. We answer the question of how to keep a given performance and use a cheaper robot (lower resources) by assigning computational tasks to the cloud infrastructure, depending on memory, computational power, and communication constraints. Also, for a fixed robot, our model provides a way to have optimal overall performance. We provide a general model for the optimal decision of algorithm allocation under certain constraints. We exemplify the model with simulation results. The main advantage of our model is that it provides an optimal task allocation simultaneously for memory and time.