Computation Task Assignment in Vehicular Fog Computing: A Learning Approach via Neighbor Advice

In the recent years, fog computing has been proposed
as a promising paradigm to enhance the performance
of latency-critical applications by hosting them within idle
computation nodes (Fog Nodes, FNs) at the edge of the network.
Such a paradigm has been further extended to the vehicular fog
computing (VFC) scenario by leveraging available computing
resources offered by modern vehicles, thus allowing them to
process different types of computation tasks on behalf of onboard
users or nearby vehicles. Within this context, the proper
assignment of computation tasks to Vehicular FNs is an important
issue that is currently under active research. To address this
issue, online learning approaches, where the performances of
the Vehicular FNs in terms of task execution delay are learnt
via trial and error, are starting to gain in popularity. This is
mainly motivated by the inherent uncertainty caused by mobility
and fluctuating resource availabilities in VFC environments.
However, since the process of learning from scratch in such a
dynamic vehicular environment may lead to a degradation in the
learning performance, this paper proposes the use of an advising
mechanism, where a roadside unit (RSU) who has already learnt
the performances of the vehicles within its range, uses its acquired
knowledge to provide advice to a neighbor RSU who does not
have enough experience allowing it to make efficient assignment
decisions on its own. To evaluate this approach, we used realistic
vehicular mobility traces to simulate the VFC scenario. The
obtained results show that our proposed approach improves the
learning performance compared to the case where no advice is
leveraged.