Computing Power and Battery Charging Management for Solar Energy Powered Edge Computing

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Abstract
The integration of energy harvesting capabilities into mobile edge computing (MEC) edge servers enables their deployment beyond the reach of electrical grids, expanding MEC services to isolated regions and geographically challenging terrains. However, the fluctuating nature of renewable energy sources, such as solar and wind, necessitates dynamic management of server computing power in response to variable energy harvesting rates. Unlike conventional models that assume predetermined amounts of harvested energy per time period, this study illustrates the complex interdependencies between server power consumption and variable energy harvesting rates due to battery charging characteristics. To address this, we introduce a novel energy harvesting model that comprehensively accounts for the interaction between computing power management and energy harvesting rates. We develop both offline and online offline optimal computing power management strategies aimed at maximizing the average computational capacity of edge servers. An analytical solution to the resulting nonlinear optimization problem is provided to determine the optimal computing power configurations. Simulation results indicate that the proposed strategy effectively balances energy harvesting rates and energy utilization, thereby enhancing computational performance in dynamic energy environments.
Funding Information
  • National Science Foundation (CNS-2122167, CNS-2122159, CNS-2006453)
  • Southeastern Center for Electrical Engineering Education (SCEEE-21-03)

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