The smart vehicles construct Vehicle of Internet which can execute various
intelligent services. Although the computation capability of the vehicle is
limited, multi-type of edge computing nodes provide heterogeneous resources for
vehicular services.When offloading the complicated service to the vehicular
edge computing node, the decision should consider numerous factors.The
offloading decision work mostly formulate the decision to a resource scheduling
problem with single or multiple objective function and some constraints, and
explore customized heuristics algorithms. However, offloading multiple data
dependency tasks in a service is a difficult decision, as an optimal solution
must understand the resource requirement, the access network, the user
mobility, and importantly the data dependency. Inspired by recent advances in
machine learning, we propose a knowledge driven (KD) service offloading
decision framework for Vehicle of Internet, which provides the optimal policy
directly from the environment. We formulate the offloading decision of
multi-task in a service as a long-term planning problem, and explores the
recent deep reinforcement learning to obtain the optimal solution. It considers
the future data dependency of the following tasks when making decision for a
current task from the learned offloading knowledge. Moreover, the framework
supports the pre-training at the powerful edge computing node and continually
online learning when the vehicular service is executed, so that it can adapt
the environment changes and learns policy that are sensible in hindsight. The
simulation results show that KD service offloading decision converges quickly,
adapts to different conditions, and outperforms the greedy offloading decision
algorithm.

提出了一个基于深度强化学习的知识驱动服务卸载决策框架，用于解决车辆互联网中多任务服务的卸载问题，并使用仿真实验证明此框架具有快速收敛、适应环境变化和优于贪心算法的性能优势。