Optimal Algorithm Allocation for Robotic Network Cloud Systems

Abstract

Cloud robotics enables robots to benefit from the massive storage and computational power of the cloud, overcoming the capacity limitations of cooperative robots. When the decision is made to use a robotic network cloud system to execute a task or a set of tasks, the main goal will be to use the cheapest, in terms of smallest memory, and fastest, in terms of shortest execution time, robots. Previous studies have mainly focused on minimizing the cost of the robots in retrieving resources by knowing the resource allocation in advance. When a task arrives in the system, it can be assigned to any processing unit, one of the robots, an eventual fog node or the cloud, and the question is where a task should be processed to optimize performance. Here, we develop a method for a robotic network cloud system to determine where each algorithm should be allocated for the system to achieve optimal performance, regardless of which robot initiates the request. We can find the minimum required memory for the robots and the optimal way to allocate the algorithms with the shortest time to complete each task. We show how our proposed method works, and we experimentaly compare its performance with a state-of-the-art method, using real world data, showing the improvements that can be obtained.