  With the rapid advancement of Intelligent Transportation Systems (ITS) and
vehicular communications, Vehicular Edge Computing (VEC) is emerging as a
promising technology to support low-latency ITS applications and services. In
this paper, we consider the computation offloading problem from mobile
vehicles/users in a heterogeneous VEC scenario, and focus on the network- and
base station selection problems, where different networks have different
traffic loads. In a fast-varying vehicular environment, computation offloading
experience of users is strongly affected by the latency due to the congestion
at the edge computing servers co-located with the base stations. However, as a
result of the non-stationary property of such an environment and also
information shortage, predicting this congestion is an involved task. To
address this challenge, we propose an on-line learning algorithm and an
off-policy learning algorithm based on multi-armed bandit theory. To
dynamically select the least congested network in a piece-wise stationary
environment, these algorithms predict the latency that the offloaded tasks
experience using the offloading history. In addition, to minimize the task loss
due to the mobility of the vehicles, we develop a method for base station
selection. Moreover, we propose a relaying mechanism for the selected network,
which operates based on the sojourn time of the vehicles. Through intensive
numerical analysis, we demonstrate that the proposed learning-based solutions
adapt to the traffic changes of the network by selecting the least congested
network, thereby reducing the latency of offloaded tasks. Moreover, we
demonstrate that the proposed joint base station selection and the relaying
mechanism minimize the task loss in a vehicular environment.
