A PSO-based Resource Allocation and Task Assignment Approach for Real-Time Cloud Computing-based Robotic Systems

Robotic systems have now become very complex because they need to integrate many real-time computing tasks like environment perception, path planning and manipulator control. Due to the limitation of computing capability in robots, a cloud computing platform is often used to provide the service to robots. How to make full use of hardware resources in cloud computing to run robots' tasks in real time become a very important problem. In this paper, we develop a particle swarm optimization approach to optimize the system's resource allocation and task assignment for the aim of minimizing system's power consumption. Specifically, in a cloud computing-based robotic system, we divide hardware resource into many identical parts, and use the virtual machine technology to create some isolated virtual machines that occupy a certain amount of hardware resource. The particle swarm optimization approach is developed to allocate hardware resource and assign real-time computing tasks to these virtual machines. The optimization aims to minimize the system's power consumption because a low-power system can reduce the service cost. We propose using 2-segment code to encode the schedule into a particle, and propose a 2-step heuristic to initialize particles. Simulation experiments show that 2-segment code makes PSO applicable to solve our problem, and 2-step heuristic improves the search efficiency and solution quality.