Green Satellite Networks Using Segment Routing and Software-Defined Networking
Bibliographic record
Abstract
This paper presents a comprehensive evaluation of network performance in software defined networking (SDN)-based low Earth orbit (LEO) satellite networks, focusing on the Telesat Lightspeed constellation. We propose a green traffic engineering (TE) approach leveraging segment routing IPv6 (SRv6) to enhance energy efficiency. Through simulations, we analyze the impact of SRv6, multi-protocol label switching (MPLS), IPv4, and IPv6 with open shortest path first (OSPF) on key network performance metrics, including peak and average CPU usage, memory consumption, packet delivery rate (PDR), and packet overhead under varying traffic loads. Results show that the proposed green TE ap-proach using SRv6 achieves notable energy efficiency, maintaining lower CPU usage and high PDR compared to traditional protocols. While SRv6 and MPLS introduce slightly higher memory usage and overhead due to their advanced configurations, these trade-offs remain manageable. Our findings highlight SRv6 with green TE as a promising solution for optimizing energy efficiency in LEO satellite networks, contributing to the development of more sustainable and efficient satellite communications.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".