  Unmanned aerial vehicle (UAV)-assisted mobile edge computing (MEC) is
emerging as a promising paradigm to provide aerial-terrestrial computing
services in close proximity 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 and NP-hard mixed integer nonlinear programming (MINLP), we
propose a two-timescale joint computing resource allocation, computation
offloading, and trajectory control (TJCCT) approach for solving the problem. In
the short timescale, we propose a price-incentive model for on-demand computing
resource allocation and a matching mechanism-based method for computation
offloading. In the long timescale, we propose a convex optimization-based
method for UAV trajectory control. Besides, we theoretically prove the
stability, optimality, and polynomial complexity of TJCCT. Extended simulation
results demonstrate that the proposed TJCCT outperforms the comparative
algorithms in terms of the system utility, average processing rate, average
completion delay, and average completion ratio.
