Energy-Constrained Satellite Edge Computing for Satellite-Terrestrial Integrated Networks

• Published in: IEEE transactions on vehicular technology Vol. 74; no. 2; pp. 3359 - 3374
• Main Authors: Cheng, Lei, Feng, Gang, Sun, Yao, Qin, Shuang, Wang, Feng, Quek, Tony Q. S.
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.02.2025; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Completion time; Computation offloading; Computational modeling; Constraints; Convex functions; Edge computing; Low earth orbit satellites; Low earth orbits; Optimization; Resource allocation; Resource management; satellite edge computing; satellite-terrestrial integrated network; Satellites; Space-air-ground integrated networks; Stochastic processes; Vehicle dynamics
• ISSN: 0018-9545; 1939-9359
• DOI: 10.1109/TVT.2024.3483203

Satellite edge computing (SEC) has emerged as an innovative paradigm for future satellite-terrestrial integrated networks (STINs), expanding computation services by sinking computing capabilities into Low-Earth-Orbit (LEO) satellites. However, the mobility of LEO satellites poses two key challenges to SEC: 1) constrained onboard computing and transmission capabilities caused by limited and dynamic energy supply, and 2) stochastic task arrivals within the satellites' coverage and time-varying channel conditions. To tackle these issues, it is imperative to design an optimal SEC offloading strategy that effectively exploits the available energy of LEO satellites to fulfill competing task demands for SEC. In this paper, we propose a dynamic offloading strategy (DOS) with the aim to minimize the overall completion time of arriving tasks in an SEC-assisted STIN, subject to the long-term energy constraints of the LEO satellite. Leveraging Lyapunov optimization theory, we first convert the original long-term stochastic problem into multiple deterministic one-slot problems parameterized by current system states. Then we use sub-problem decomposition to jointly optimize the task offloading, computing, and communication resource allocation strategies. We theoretically prove that DOS achieves near-optimal performance. Numerical results demonstrate that DOS significantly outperforms the other four baseline approaches in terms of task completion time and dropping rate.