  The energy sustainability of multi-access edge computing (MEC) platforms is
here addressed by developing Energy-Aware job Scheduling at the Edge (EASE), a
computing resource scheduler for edge servers co-powered by renewable energy
resources and the power grid. The scenario under study involves the optimal
allocation and migration of time-sensitive computing tasks in a
resource-constrained internet of vehicles (IoV) context. This is achieved by
tackling, as the main objective, the minimization of the carbon footprint of
the edge network, whilst delivering adequate quality of service (QoS) to the
end users (e.g., meeting task execution deadlines). EASE integrates i) a
centralized optimization step, solved through model predictive control (MPC),
to manage the renewable energy that is locally collected at the edge servers
and their local computing resources, estimating their future availability, and
ii) a distributed consensus step, solved via dual ascent in closed form, to
reach agreement on service migrations. EASE is compared with four existing
migration strategies. Quantitative results demonstrate its greater energy
efficiency, which often gets close to complete carbon neutrality, while also
improving the QoS.
