Multi-objective Computation Offloading for Cloud Robotics using NSGA-II

With the emergence of cloud robotics, computation offloading presents a new trend in cloud computing that has been applied to robots; to provide them with resources for performing computationally intensive tasks. In most scientific research, the main objectives behind computation offloading are reducing energy consumption and minimizing the execution time of robotics applications. However, these two metrics are conflicting, and optimizing them simultaneously is challenging. Reducing energy consumption may lead to a rise in the completion time, and vice-versa. In this paper, we consider the problem of optimization of energy consumption and completion time in a cloud robotic system. We formulated the offloading decision as a multi-objective optimization problem. We further adapted the Non-dominated Sorting Genetic Algorithm (NSGA-II) to find a set of Paretooptimal solutions. Through simulations, we demonstrated that our offloading solution can save 80% of the robot’s energy consumption; and reduce 70% of the application completion time. We proved also the adaptability of the model against bandwidth changes.