Contract-Based Computing Resource Management via Deep Reinforcement Learning in Vehicular Fog Computing

Abstract

Vehicle fog computing (VFC) is proposed as a solution that can significantly reduce the task processing overload of base station during the peak time, where the vehicle as a fog node contributes idle computing resource for task processing. However, there are still many challenges in the deployment of VFC, such as the lack of specific incentives of resource contribution, high system complexity, and offloading collisions between vehicles when the vehicles are offloading tasks simultaneously. In this paper, we first propose a novel contract-based incentive mechanism that combines resource contribution and resource utilization. Based on that, we propose to use distributed deep reinforcement learning to allocate resources and reduce system complexity. Task offloading method based on the queuing model is also proposed to avoid decision collisions in multi-vehicles task offloading. Numerical experiment results demonstrate that our proposed scheme has achieved a significant improvement in task offloading and resource allocation performance.

Keywords

• Vehicular fog computing
• contract theory
• deep reinforcement learning
• resource allocation
• task offloading