  Recent years have witnessed the emergence of mobile edge computing (MEC), on
the premise of a cost-effective enhancement in the computational ability of
hardware-constrained wireless devices (WDs) comprising the Internet of Things
(IoT). In a general multi-server multi-user MEC system, each WD has a
computational task to execute and has to select binary (off)loading decisions,
along with the analog-amplitude resource allocation variables in an online
manner, with the goal of minimizing the overall energy-delay cost (EDC) with
dynamic system states. While past works typically rely on the explicit
expression of the EDC function, the present contribution considers a practical
setting, where in lieu of system state information, the EDC function is not
available in analytical form, and instead only the function values at queried
points are revealed. Towards tackling such a challenging online combinatorial
problem with only bandit information, novel Bayesian optimization (BO) based
approaches are put forth by leveraging the multi-armed bandit (MAB) framework.
Per time slot, the discrete offloading decisions are first obtained via the MAB
method, and the analog resource allocation variables are subsequently optimized
using the BO selection rule. By exploiting both temporal and contextual
information, two novel BO approaches, termed time-varying BO and contextual
time-varying BO, are developed. Numerical tests validate the merits of the
proposed BO approaches compared with contemporary benchmarks under different
MEC network sizes.
