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.
翻译:为了让机器人执行任务,有时需要同时执行几种算法。 算法可以由机器人自己操作, 也可以应要求在云层基础设施上操作。 云层基础设施一词用来描述与云计算有关的硬件、 存储、 抽象资源和网络资源。 取决于关于算法执行地点的决定, 机器人的总体执行时间和必要的记忆空间将相应改变。 机器人的价格取决于其记忆能力和计算能力等。 我们回答如何保持某一性能和使用更廉价的机器人( 更低的资源) 的问题, 方法是根据记忆、 计算能力和通信限制, 向云层基础设施分配计算任务。 另外, 对于固定的机器人, 我们的模式提供了一种最佳的整体性能。 我们为在某些限制下最佳的算法分配提供一种一般模式。 我们用模拟结果来示范模型。 我们模型的主要优点是它为记忆和时间同时提供最佳的任务分配。