  Mobile edge computing (MEC) is a promising paradigm to accommodate the
increasingly prosperous delay-sensitive and computation-intensive applications
in 5G systems. To achieve optimum computation performance in a dynamic MEC
environment, mobile devices often need to make online decisions on whether to
offload the computation tasks to nearby edge terminals under the uncertainty of
future system information (e.g., random wireless channel gain and task
arrivals). The design of an efficient online offloading algorithm is
challenging. On one hand, the fast-varying edge environment requires frequently
solving a hard combinatorial optimization problem where the integer offloading
decision and continuous resource allocation variables are strongly coupled. On
the other hand, the uncertainty of future system states makes it hard for the
online decisions to satisfy long-term system constraints. To address these
challenges, this article overviews the existing methods and introduces a novel
framework that efficiently integrates model-based optimization and model-free
learning techniques. Besides, we suggest some promising future research
directions of online computation offloading control in MEC networks.
