Distributed and Load Adaptive Energy Management Algorithm for Ethernet Green Routers
Bibliographic record
Abstract
The existing Ethernet networks are designed with high redundancy and over-dimensioning so they can provide reliable services during peak traffic demand periods. However, this has increased the total energy consumption and operational cost. In this paper, we propose an energy saving algorithm (ESA) to reduce the energy consumption of green routers by considering the buffer status and the traffic load. We develop a Network Simulator, version 2, (NS-2)–based simulation model for ESA to evaluate its performance with respect to real traffic traces. Performance bounds of the proposed algorithm are derived. Numerical evaluations are conducted to verify the accuracy of the simulation model against derived bounds. Performance evaluations demonstrate that the proposed algorithm outperforms candidate algorithms, thereby providing greater energy savings with an acceptable packet delay and loss. We show that the introduced delay is bounded by an upper bound that is slightly larger than half of the sleep timer. Furthermore, performance comparisons are extensive and detailed, thus providing insights into the performance of different energy saving functions considered by the candidate algorithms.
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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 | 0.000 | 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 | 0.001 | 0.001 |
| Open science | 0.001 | 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".