Distributed V2V Computation Offloading Based on Dynamic Pricing Using Deep Reinforcement Learning

Abstract

Vehicular computation offloading is a promising paradigm that improves the computing capability of vehicles to support autonomous driving and various on-board infotainment services. Comparing with accessing the remote cloud, distributed vehicle-to-vehicle (V2V) computation offloading is more efficient and suitable for delay-sensitive tasks by taking advantage of vehicular idle computing resources. Due to the high dynamic vehicular environment and the variation of available vehicular computing resources, it is a great challenge to design an effective task offloading mechanism to efficiently utilize vehicular computing resources. In this paper, we investigate the computation task allocation among vehicles, and propose a distributed V2V computation offloading framework, in which wireless channel states and variation of idle computing resources are both considered. Specially, we formulate the task allocation problem as a sequential decision making problem, which can be solved by using deep reinforcement learning. Considering that vehicles with idle computing resources may not share their computing resources voluntarily, we thus propose a dynamic pricing scheme that motivates vehicles to contribute their computing resources according to the price they receive. The performance of designed task allocation mechanism is validated by simulation results which reveal the effectiveness of our mechanism compared to the other algorithms.