The vehicular edge computing (VEC) system integrates the computing resources
of vehicles, and provides computing services for other vehicles and pedestrians
with task offloading. However, the vehicular task offloading environment is
dynamic and uncertain, with fast varying network topologies, wireless channel
states and computing workloads. These uncertainties bring extra challenges to
task offloading. In this work, we consider the task offloading among vehicles,
and propose a solution that enables vehicles to learn the offloading delay
performance of their neighboring vehicles while offloading computation tasks.
We design an adaptive learning-based task offloading (ALTO) algorithm based on
the multi-armed bandit (MAB) theory, in order to minimize the average
offloading delay. ALTO works in a distributed manner without requiring frequent
state exchange, and is augmented with input-awareness and occurrence-awareness
to adapt to the dynamic environment. The proposed algorithm is proved to have a
sublinear learning regret. Extensive simulations are carried out under both
synthetic scenario and realistic highway scenario, and results illustrate that
the proposed algorithm achieves low delay performance, and decreases the
average delay up to 30% compared with the existing upper confidence bound based
learning algorithm.

本论文针对汽车间的计算任务卸载环境动态不确定的挑战，提出了一种基于自适应学习的任务卸载算法，在多臂赌博理论基础上，利用邻近车辆的卸载延迟性能进行学习并且能够适应动态环境，实现了分布式协作的任务卸载，并证明了具有子线性的学习遗憾。在综合考虑各种不确定性的条件下，实验结果表明，相比现有的以置信上限为基础的学习算法，该算法使任务卸载延迟性能更优，平均延迟降低了高达 30%。