Allocation of Computing Tasks in Distributed MEC Servers Co-Powered by Renewable Sources and the Power Grid

We consider a Multiaccess Edge Computing (MEC) network where distributed servers have energy harvesting (e.g., solar) and storage (e.g., batteries) capabilities. Energy from a connected power grid is also available, in case that harvested from ambient sources is scarce or absent. Network processors are deployed according to a given network topology, across two tiers, and computing tasks are flexibly allocated depending on considerations related to load balancing, energy consumption (for communication and computing) and energy purchases from the power grid. Specifically, an on-line optimization problem, exploiting a predictive control approach, is formulated to minimize the monetary cost incurred in the energy purchases from the power grid, by dispatching the computation jobs to those servers that have enough energy and computation resources. Our proposed framework uses forecasts of exogenous processes, such as the amount of energy harvested and job arrivals, which are estimated on the fly to steer the allocation of computation jobs to the servers.