  Vehicular cloud computing (VCC) is proposed to effectively utilize and share
the computing and storage resources on vehicles. However, due to the mobility
of vehicles, the network topology, the wireless channel states and the
available computing resources vary rapidly and are difficult to predict. In
this work, we develop a learning-based task offloading framework using the
multi-armed bandit (MAB) theory, which enables vehicles to learn the potential
task offloading performance of its neighboring vehicles with excessive
computing resources, namely service vehicles (SeVs), and minimizes the average
offloading delay. We propose an adaptive volatile upper confidence bound
(AVUCB) algorithm and augment it with load-awareness and occurrence-awareness,
by redesigning the utility function of the classic MAB algorithms. The proposed
AVUCB algorithm can effectively adapt to the dynamic vehicular environment,
balance the tradeoff between exploration and exploitation in the learning
process, and converge fast to the optimal SeV with theoretical performance
guarantee. Simulations under both synthetic scenario and a realistic highway
scenario are carried out, showing that the proposed algorithm achieves
close-to-optimal delay performance.
