2019/02/04 by Thembelihle Dlamini, Ángel Fernández Gambı́n, Dlamini, Thembelihle +5
Computer Science · Engineering · #Age of Information Optimization #Energy Harvesting in Wireless Networks #FOS: Electrical engineering #IoT and Edge/Fog Computing #Signal Processing (eess.SP) #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.1902.05358
openalex publication_date 2019/02/14 · openalex created_date 2022/07/29 · openalex updated_date 2026/07/28
The convergence of communication and computing has lead to the emergence of\nMulti-access Edge Computing (MEC), where computing resources (supported by\nVirtual Machines (VMs)) are distributed at the edge of the Mobile Network (MN),\ni.e., in Base Stations (BSs), with the aim of ensuring reliable and ultra-low\nlatency services. Moreover, BSs equipped with Energy Harvesting (EH) systems\ncan decrease the amount of energy drained from the power grid resulting in\nenergetically self-sufficient MNs. The combination of these paradigms is\nconsidered here. Specifically, we propose an online optimization algorithm,\ncalled ENergy Aware and Adaptive Management (ENAAM), based on foresighted\ncontrol policies exploiting (short-term) traffic load and harvested energy\nforecasts, where BSs and VMs are dynamically switched on/off towards energy\nsavings and QoS provisioning. Our numerical results reveal that ENAAM achieves\nenergy savings with respect to the case where no energy management is applied,\nranging from 57% and 69%. Moreover, the extension of ENAAM within a cluster of\nBSs provides a further gain ranging from 9% to 16% in energy savings with\nrespect to the optimization performed in isolation for each BS.\n