  Future Mobile Networks (MNs), 5G and beyond 5G, will require a paradigm shift
from traditional resource allocation mechanisms as Base Stations (BSs) will be
empowered with computation capabilities (i.e., offloading and computation is
performed closer to mobile users). This is motivated by the expected data
explosion in the volume, variety, and velocity, generated by pervasive mobile
and Internet of Things (IoT) devices at the network edge. Towards efficient
resource management, within the Multi-access Edge Computing (MEC) paradigm, we
make use of the Long Short-Term Memory (LSTM) neural network for time series
forecasting and control-theoretic techniques for foresighted optimization, thus
bringing intelligent mechanisms for handling network resources within the
network edge. Here, we propose online algorithms for autoscaling and
reconfiguring the computing-plus-communication resources within the virtualized
computing platform, and also enable dynamic switching on/off BSs by taking into
account the forecasted traffic load and harvested energy. The main goal is to
minimize the overall energy consumption, with a guarantee of Quality of Service
(QoS). Our numerical results, obtained through trace-driven simulations, show
that the proposed optimization strategies lead to a considerable reduction in
the energy consumed by the edge computing and communication facilities,
promoting energy self-sustainability within the MN through the use of green
energy.
