  We consider a multi-user multi-server mobile edge computing (MEC) system, in
which users arrive on a network randomly over time and generate computation
tasks, which will be computed either locally on their own computing devices or
be offloaded to one of the MEC servers. Under such a dynamic network
environment, we propose a novel task offloading policy based on hybrid
online-offline learning, which can efficiently reduce the overall computation
delay and energy consumption only with information available at nearest MEC
servers from each user. We provide a practical signaling and learning framework
that can train deep neural networks for both online and offline learning and
can adjust its offloading policy based on the queuing status of each MEC server
and network dynamics. Numerical results demonstrate that the proposed scheme
significantly reduces the average computation delay for a broad class of
network environments compared to the conventional offloading methods. It is
further shown that the proposed hybrid online-offline learning framework can be
extended to a general cost function reflecting both delay and energy-dependent
metrics.
