  This paper investigates distributed processing in Vehicular Edge Cloud
(VECs), where a group of vehicles in a car park, at a charging station or at a
road traffic intersection, cluster and form a temporary vehicular cloud by
combining their computational resources in the cluster. We investigated the
problem of energy efficient processing task allocation in VEC by developing a
Mixed Integer Linear Programming (MILP) model to minimize power consumption by
optimizing the allocation of different processing tasks to the available
network resources, cloud resources, fog resources and vehicular processing
nodes resources. Three dimensions of processing allocation were investigated.
The first dimension compared centralized processing (in the central cloud) to
distributed processing (in the multi-layer fog nodes). The second dimension
introduced opportunistic processing in the vehicular nodes with low and high
vehicular node density. The third dimension considered non-splittable tasks
(single allocation) versus splittable tasks (distributed allocation),
representing real-time versus non real-time applications respectively. The
results revealed that a power savings up to 70% can be achieved by allocating
processing to the vehicles. However, many factors have an impact on the power
saving such the vehicle processing capacities, vehicles density, workload size,
and the number of generated tasks. It was observed that the power saving is
improved by exploiting the flexibility offered by task splitting among the
available vehicles.
