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Multi-Access Edge Computing (MEC) is a paradigm for handling delay sensitiveservices that require ultra-low latency at the access network. With it,computing and communications are performed within one Base Station (BS) site,where the computation resources are in the form of Virtual Machines (VMs)(computer emulators) in the MEC server. MEC and Energy Harvesting (EH) BSs,i.e., BSs equipped with EH equipments, are foreseen as a key towardsnext-generation mobile networks. In fact, EH systems are expected to decreasethe energy drained from the electricity grid and facilitate the deployment ofBSs in remote places, extending network coverage and making energyself-sufficiency possible in remote/rural sites. In this paper, we propose anonline optimization algorithm called ENergy Aware and Adaptive Management(ENAAM), for managing remote BS sites through foresighted control policiesexploiting (short-term) traffic load and harvested energy forecasts. Ournumerical results reveal that ENAAM achieves energy savings with respect to thecase where no energy management is applied, ranging from 56% to 66% through thescaling of computing resources, and keeps the server utilization factor between30% and 96% over time (with an average of 75%). Notable benefits are also foundagainst heuristic energy management techniques.

Online Resource Management in Energy Harvesting BS Sites through Prediction and Soft-Scaling of Computing Resources