  Vehicular fog computing (VFC) pushes the cloud computing capability to the
distributed fog nodes at the edge of the Internet, enabling compute-intensive
and latency-sensitive computing services for vehicles through task offloading.
However, a heterogeneous mobility environment introduces uncertainties in terms
of resource supply and demand, which are inevitable bottlenecks for the optimal
offloading decision. Also, these uncertainties bring extra challenges to task
offloading under the oblivious adversary attack and data privacy risks. In this
article, we develop a new adversarial online algorithm with bandit feedback
based on the adversarial multi-armed bandit theory, to enable scalable and
low-complex offloading decision making on the fog node selection toward
minimizing the offloading service cost in terms of delay and energy. The key is
to implicitly tune exploration bonus in selection and assessment rules of the
designed algorithm, taking into account volatile resource supply and demand. We
theoretically prove that the input-size dependent selection rule allows to
choose a suitable fog node without exploring the sub-optimal actions, and also
an appropriate score patching rule allows to quickly adapt to evolving
circumstances, which reduces variance and bias simultaneously, thereby
achieving better exploitation exploration balance. Simulation results verify
the effectiveness and robustness of the proposed algorithm.
