Battery-Aware Energy Optimization for Satellite Edge Computing
Qing Li, Shangguang Wang, Xiao Ma, Ao Zhou, Yue Wang, Gang Huang, Xuanzhe Liu
Abstract
Satellite edge computing can incur dramatically increased energy demand onboard, which is met by satellite batteries during eclipses. Excessive energy usage during regular operations accelerates battery wear. Therefore, it is important and timely to optimize the energy consumption onboard to extend satellite batteries life. This paper investigates battery-aware energy optimization for satellite edge computing under energy harvesting dynamics and wireless environment uncertainty. Inspired by the periodical energy harvesting and satellite-ground connection, we develop a pattern-aware online energy scheduling algorithm within an online convex optimization framework. This learning algorithm achieves theoretical guarantees of no regret and gradually zeroing constraint violations. We further exploit inter-satellites collaboration to extend the average battery life in a whole constellation where satellites have different battery capacity degradation. Trace-driven simulations show that our algorithm can significantly extend the battery life by 1.32× and effectively adapt to the energy harvesting dynamics and wireless environment uncertainty.