Renewables Power the Orbit? Achieving Sustainable Space Edge Computing via QoS-Aware Offloading

2026-05-04

AI Summary

This study addresses the challenge of unsustainable on-board computation in low Earth orbit (LEO) satellites for 6G networks, which accelerates battery degradation, alongside the global underutilization of renewable energy due to transmission constraints. To tackle these issues, the paper proposes SQSO, a quality-of-service-aware sustainable offloading framework that pioneers the co-design of satellite networks and renewable energy infrastructure. SQSO adaptively offloads mission-critical data to data centers co-located with curtailed renewable power stations, leveraging spatiotemporal alignment between satellite passes and surplus clean energy availability. Under dynamic network topology and time-varying electricity pricing, the framework introduces an interval-based model and an adaptive offloading orchestration algorithm (AO²). Experimental results, based on Starlink-scale simulations and real-world electricity price data, demonstrate that SQSO reduces energy consumption by up to 76.03%, battery wear by 76.85%, and task latency compared to state-of-the-art approaches.

Abstract

Low-Earth-Orbit (LEO) satellite constellations are becoming integral to 6G infrastructure, but increasing in-orbit computation accelerates battery degradation and raises sustainability concerns. Meanwhile, renewable-heavy regions worldwide experience persistent energy curtailment due to transmission bottlenecks, leaving substantial clean energy stranded near generation sites. We identify a satellite-grid co-design opportunity: adaptively offloading task-critical data from satellite to data centers co-located with renewable power plants. However, realizing this vision requires jointly considering intermittent and capacity-limited communication windows, as well as time-varying electricity budgets. In this paper, we propose SQSO, a Sustainable and QoS-aware Satellite Offloading framework that models per-interval task offloading as a constrained optimization over dynamic topology and electricity prices. Under this framework, we design $\text{AO}^2$, an adaptive offloading orchestration algorithm to solve the formulated optimization problem. Using Starlink-scale simulations and real-world electricity price traces, $\text{AO}^2$ reduces energy consumption by up to 76.03% and battery life consumption by up to 76.85% compared to state-of-the-art schemes, while also lowering task delay. This work highlights that sustainable scaling of LEO constellations requires co-design of space networking and renewable energy infrastructure, while our solution promotes renewable-aware task offloading and cross-domain collaboration for space-energy integration in the 6G era.

Problem

• LEO satellite
• sustainable computing
• renewable energy curtailment
• space-edge computing
• energy sustainability

Innovation

• satellite offloading
• renewable energy integration
• LEO constellations
• QoS-aware optimization
• sustainable computing