CPU-Utilization-Aware Scheduling for In-Vehicle Distributed Computing

With the rapid advancement of intelligent vehicle technology, the demand for vehicular computing power is increasing. To alleviate computing loads on the on-board computer, this paper proposes an in-vehicle distributed computing system that leverages in-vehicle devices, such as smartphones and tablet computers, for cooperative task execution. Different from previous works that focused on the impact of CPU frequency management for computation offloading, we consider the scenario where the CPU frequency of devices cannot be adjusted, and study a more practical approach to make offloading and scheduling decisions by considering the CPU utilization of devices. Therefore, we first derive an analytical relationship between CPU utilization and computation latency, and then propose a CPU Utilization-Aware Scheduling (CUAS) policy to minimize response latency consisting of computation and communication latency. Simulations conducted on Simgrid show that our proposed CUAS policy can reduce the response latency by up to 20.60% compared with the benchmarks. Additionally, we established a real-world testbed to validate our system's practicality. Experimental results indicate that our proposed policy can reduce response latency by up to 20.75% compared with the benchmarks.