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arxiv: 2605.03232 · v1 · submitted 2026-05-04 · 💻 cs.NI

Recognition: unknown

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

(2) School of Computing Science, (3) Institute of Space Internet, Canada, China, China), Chongqing Kang (1), Ershun Du (1), Fudan University, Hao Fang (2), Haoyuan Zhao (2), Jiangchuan Liu (2) ((1) Department of Electrical Engineering, Long Chen (2), Simon Fraser University, Tsinghua University, Xiaoyi Fan (1), Yi Ching Chou (2), Zhe Chen (3)

Pith reviewed 2026-05-08 16:57 UTC · model grok-4.3

classification 💻 cs.NI
keywords LEO satellitesrenewable energytask offloadingQoS-awareedge computingsustainable space systemsbattery lifeStarlink simulation
0
0 comments X

The pith

Offloading LEO satellite tasks to renewable-powered ground data centers cuts energy use by up to 76 percent while meeting delay requirements.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper shows that LEO satellite constellations can reduce their heavy battery drain by shifting computation to ground data centers sited at renewable power plants. This move requires handling short, intermittent satellite communication windows and shifting electricity prices and availability. The authors model the decisions as an optimization problem and solve it with their AO² algorithm. Starlink-scale simulations using real electricity traces demonstrate large drops in energy and battery consumption plus shorter task delays. If the approach works at scale, it would let satellite networks grow without exhausting onboard power or wasting stranded clean energy on the ground.

Core claim

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 AO², an adaptive offloading orchestration algorithm to solve the formulated optimization problem. Using Starlink-scale simulations and real-world electricity price traces, AO² 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.

What carries the argument

The AO² adaptive offloading orchestration algorithm, which solves a constrained optimization problem that decides per-interval task offloading while respecting dynamic satellite communication windows and time-varying electricity budgets from co-located renewables.

Load-bearing premise

Renewable power plants can be practically co-located with suitable data centers and that intermittent communication windows plus time-varying electricity budgets can be modeled accurately enough for the optimization to deliver real-world gains.

What would settle it

A real-world pilot with actual LEO satellites and renewable sites that measures whether energy and battery savings stay above 50 percent without violating task delay targets under live grid and weather conditions would settle the claim.

Figures

Figures reproduced from arXiv: 2605.03232 by (2) School of Computing Science, (3) Institute of Space Internet, Canada, China, China), Chongqing Kang (1), Ershun Du (1), Fudan University, Hao Fang (2), Haoyuan Zhao (2), Jiangchuan Liu (2) ((1) Department of Electrical Engineering, Long Chen (2), Simon Fraser University, Tsinghua University, Xiaoyi Fan (1), Yi Ching Chou (2), Zhe Chen (3).

Figure 1
Figure 1. Figure 1: Task-critical data on-demand offloading from satellites view at source ↗
Figure 2
Figure 2. Figure 2: Spatial distribution and capacity of power plants in view at source ↗
Figure 4
Figure 4. Figure 4: Electricity price over a day and the heatmap of view at source ↗
Figure 5
Figure 5. Figure 5: GSL availability windows of the satellite and the view at source ↗
Figure 6
Figure 6. Figure 6: Overview of the Sustainable and QoS-aware Satellite view at source ↗
Figure 7
Figure 7. Figure 7: Overview of the AO2 algorithm. Proof. The initialization in lines 1–2 incurs a time complex￾ity of O(|V S(τ )| log |V S(τ )|) = O(|VS| log |VS|) iterations, where the equality holds since V S(τ ) ⊆ VS. For each satellite v ∈ V S(τ ), we determine the target ground station for task offloading, and this process for a single satellite v has a time complexity of O(|Rv(τ )| · |ES(τ )|). The orchestration stage … view at source ↗
Figure 8
Figure 8. Figure 8: CDF of total energy consumption per interval. 0.0 0.1 0.2 0.3 0.4 0.5 Life consumption per interval 0.00 0.25 0.50 0.75 1.00 CDF AO² CCT HROA view at source ↗
Figure 12
Figure 12. Figure 12: Energy consumption in different budget constraints. 2.5 5.0 7.5 10.0 12.5 15.0 Normalized budget constraint 10 20 30 40 Total life consumption AO² CCT HROA view at source ↗
Figure 15
Figure 15. Figure 15: Average delay in dif￾ferent budget constraints. Runtime Starlink satellites 1584 2376 3168 4752 5544 Avg. runtime per interval (s) 10.87 14.95 17.87 26.09 30.66 Std. runtime per interval (s) 1.62 2.73 1.55 1.12 1.24 Avg. runtime per task (ms) 10.75 9.91 8.87 8.65 8.68 Std. runtime per task (ms) 1.64 1.85 0.73 0.34 0.30 TABLE I: Algorithm runtime across network sizes. straints. When budget constraints are … view at source ↗
read the original 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.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 1 minor

Summary. The paper proposes the SQSO framework and AO² algorithm for QoS-aware offloading of tasks from LEO satellites to ground data centers co-located with renewable power plants. It models per-interval offloading decisions as a constrained optimization problem incorporating dynamic satellite topology, intermittent capacity-limited communication windows, and time-varying electricity prices. Starlink-scale simulations using real-world electricity price traces are used to claim that AO² achieves up to 76.03% reduction in energy consumption and 76.85% reduction in battery life consumption relative to state-of-the-art schemes, while also lowering task delay.

Significance. If the modeling of communication constraints proves accurate, the work identifies a novel cross-domain opportunity to co-design LEO space networking with renewable energy infrastructure, addressing both in-orbit sustainability and terrestrial energy curtailment. The use of large-scale Starlink simulations and real electricity traces provides a concrete, falsifiable basis for the performance claims and highlights practical integration challenges in 6G-era systems.

major comments (2)
  1. [Abstract and framework description] Abstract and framework description: the headline claims of 76.03% energy and 76.85% battery-life reductions are obtained from simulations of the AO² solver on the constrained optimization. However, it is not shown that the model enforces finite bandwidth, propagation delay, and strict closure of LEO visibility windows such that each offloaded task's data volume must complete transfer within the window. If these constraints are relaxed or omitted, the optimizer can schedule infeasible transfers, directly inflating the reported savings. This modeling detail is load-bearing for the central performance claims.
  2. [Optimization formulation] The abstract states concrete percentage improvements from simulations, but the full optimization formulation (including objective, constraints, data exclusion rules, and AO² solver verification) is not provided in sufficient detail to reproduce or assess the support for the claims.
minor comments (1)
  1. [Evaluation] The comparison baselines labeled 'state-of-the-art schemes' should be explicitly named and briefly described in the evaluation section to allow readers to assess the delta.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the detailed and constructive feedback on our manuscript. The comments correctly identify areas where additional clarity on modeling constraints and formulation details will strengthen the paper. We address each point below and commit to revisions that enhance transparency without altering the core contributions or results.

read point-by-point responses

  1. Referee: [Abstract and framework description] Abstract and framework description: the headline claims of 76.03% energy and 76.85% battery-life reductions are obtained from simulations of the AO² solver on the constrained optimization. However, it is not shown that the model enforces finite bandwidth, propagation delay, and strict closure of LEO visibility windows such that each offloaded task's data volume must complete transfer within the window. If these constraints are relaxed or omitted, the optimizer can schedule infeasible transfers, directly inflating the reported savings. This modeling detail is load-bearing for the central performance claims.

    Authors: We appreciate the referee's emphasis on ensuring the communication constraints are rigorously enforced, as this is indeed central to the validity of our performance claims. The SQSO framework models per-interval offloading as a constrained optimization that explicitly incorporates finite bandwidth, propagation delays, and strict LEO visibility window closures. For each task, the formulation requires that the full data volume completes transfer within the window via time-dependent capacity constraints (derived from dynamic topology and window durations) and feasibility checks that exclude partial or infeasible transfers. The AO² algorithm solves only within this feasible region. To eliminate any ambiguity, we will revise the framework description section to include the explicit constraint equations for window closure and bandwidth limits, along with a short illustrative example showing how infeasible schedules are prevented. This will make the enforcement transparent while preserving the reported savings, which are computed exclusively on feasible solutions. revision: yes

  2. Referee: [Optimization formulation] The abstract states concrete percentage improvements from simulations, but the full optimization formulation (including objective, constraints, data exclusion rules, and AO² solver verification) is not provided in sufficient detail to reproduce or assess the support for the claims.

    Authors: We agree that the current presentation of the optimization formulation lacks sufficient detail for full reproducibility and independent assessment of the claims. While the manuscript describes the high-level constrained optimization and AO² algorithm, it does not provide the complete mathematical program, all constraints, data exclusion rules for tasks that cannot meet QoS or window requirements, or explicit verification steps for the solver. In the revised manuscript, we will expand Section 3 to include the full objective function, the complete set of constraints (communication, energy, QoS, and topology), data exclusion logic, pseudocode for AO², and verification details such as how the solver ensures feasibility and any bounding or approximation techniques used. These additions will directly support the simulation results and address the referee's concern. revision: yes

Circularity Check

0 steps flagged

No circularity; standard constrained optimization over external traces

full rationale

The paper's core chain is: (1) identify co-location opportunity from external renewable curtailment and LEO topology facts, (2) formulate per-interval offloading as a constrained optimization whose inputs are dynamic topology and real-world electricity price traces, (3) design AO² algorithm to solve the optimization, (4) evaluate via Starlink-scale simulation against those same external traces. No equation reduces to a self-definition, no fitted parameter is relabeled as a prediction, and no load-bearing premise rests on a self-citation chain. The reported energy and battery reductions are simulation outputs that remain falsifiable against the input traces.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Abstract-only review; no information available on free parameters, axioms, or invented entities.

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discussion (0)

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