Online Service Deployment on Mega-LEO Satellite Constellations for End-to-End Delay Optimization
Bibliographic record
Abstract
Satellite Edge Computing (SEC), which empowers satellites with computing capabilities, has been regarded as a promising paradigm for 6G alongside the development of mega-Low Earth Orbit (LEO) satellite constellations. With SEC, tasks traditionally performed on the ground can be offloaded and processed in the sky. Currently, most existing studies assume that the service entities have been deployed on satellites in advance, ignoring the selection of specific serving nodes. However, as the communication and computing resources of satellites vary in accordance with time, selecting the optimal node for deploying the service entity and migrating it at the right time will significantly affect users' quality of experience. In this regard, this article investigates the online service deployment on mega-LEO satellite constellations, taking into account the time-varying on-board resources and limited visible time. We formulate an optimization problem to maximize the number of successful deployments that meet delay requirements for each user under the long-term migration cost constraint. To solve this problem, we transform it into a Markov decision process and propose COMPOSE, an online satellite service deployment scheme based on convolution-proximal policy optimization. COMPOSE dynamically selects the optimal serving node and migrates the service entity in due course. The simulation results demonstrate the superior performance of COMPOSE in terms of delay satisfaction ratio, delay variance, and the number of migrations.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".