  The Software-Defined Air-Ground integrated Vehicular (SD-AGV) networks have
emerged as a promising paradigm, which realize the flexible on-ground resource
sharing to support innovative applications for UAVs with heavy computational
overhead. In this paper, we investigate a vehicular cloud-assisted task
scheduling problem in SD-AGV networks, where the computation-intensive tasks
carried by UAVs, and the vehicular cloud are modeled via graph-based
representation. To map each component of the graph tasks to a feasible vehicle,
while achieving the trade-off among minimizing UAVs' task completion time,
energy consumption, and the data exchange cost among moving vehicles, we
formulate the problem as a mixed-integer non-linear programming problem, which
is Np-hard. Moreover, the constraint associated with preserving task structures
poses addressing the subgraph isomorphism problem over dynamic vehicular
topology, that further complicates the algorithm design. Motivated by which, we
propose an efficient decoupled approach by separating the template (feasible
mappings between components and vehicles) searching from the transmission power
allocation. For the former, we present an efficient algorithm of searching for
all the isomorphic subgraphs with low computation complexity. For the latter,
we introduce a power allocation algorithm by applying $p$-norm and convex
optimization techniques. Extensive simulations demonstrate that the proposed
approach outperforms the benchmark methods considering various problem sizes.
