  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.
