Green Network Traffic Engineering Using Segment Routing: an Experiment Report
Bibliographic record
Abstract
With the ever-expanding network-based services, environmental impact has become a concern, as the surge in network traffic between devices has only intensified in increased energy consumption. This paper aims to exploit Segment Routing over IPv6 (SRv6) for energy efficiency purposes for data forwarding. SRv6 is a traffic engineering mechanism that enables data packet steering using segments in IPv6 headers. The main idea of the paper is to use SRv6 with automatic rerouting of network traffic based on the resource usage of network devices for higher energy efficiency compared to the traditional IP forwarding based on the shortest path first (SPF) algorithm. The method and system outlined in this paper dynamically created network topologies within Mininet and performed SRv6 using the ROSE platform to route packets through the most energy-efficient paths, all while actively collecting device usages, calculating dynamic weights, computing energy-efficient paths, and rerouting the traffic using SRv6. This paper successfully achieved the goal of energy-aware traffic rerouting. The results showed that the resource usage for SRv6 could be more than 70% lower than that of the SPF-based forwarding, depending on the network topology.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".