  Unmanned aerial vehicles (UAV)-assisted mobile edge computing (MEC) is
emerging as a promising paradigm to provide aerial-terrestrial computing
services close to mobile devices (MDs). However, meeting the demands of
computation-intensive and delay-sensitive tasks for MDs poses several
challenges, including the demand-supply contradiction between MDs and MEC
servers, the demand-supply heterogeneity between MDs and MEC servers, the
trajectory control requirements on energy efficiency and timeliness, and the
different time-scale dynamics of the network. To address these issues, we first
present a hierarchical architecture by incorporating terrestrial-aerial
computing capabilities and leveraging UAV flexibility. Furthermore, we
formulate a joint computing resource allocation, computation offloading, and
trajectory control problem to maximize the system utility. Since the problem is
a non-convex mixed integer nonlinear programming (MINLP), we propose a two
time-scale joint computing resource allocation, computation offloading, and
trajectory control (TJCCT) approach. In the short time scale, we propose a
price-incentive method for on-demand computing resource allocation and a
matching mechanism-based method for computation offloading. In the long time
scale, we propose a convex optimization-based method for UAV trajectory
control. Besides, we prove the stability, optimality, and polynomial complexity
of TJCCT. Simulation results demonstrate that TJCCT outperforms the comparative
algorithms in terms of the utility of the system, the QoE of MDs, and the
revenue of MEC servers.
