A distributed framework for energy-efficient lightpaths in computational grids
Bibliographic record
Abstract
Over the past decade, the ever-increasing energy demands of IT infrastructures have posed significant challenges for the research community in terms of reducing their total power consumption and minimizing their environmental impact. Optical communication networks are envisioned to be promising candidates to help preventing this problem affecting the Internet backbone, as well as for distributed applications such as computational grids. In this paper, we propose an adaptive and distributed scheme for the establishment of energy-efficient lightpaths in computational grids. The grid is deployed over an optical circuit-switched backbone network, defining an optical grid network. Each node of the backbone network maintains two different dynamic thresholds values and estimates the changes in network performance by evaluating the moving average of the total wavelength channel occupancy on all its input/output links. The nodes have the ability of reducing the energy consumption by entering into an Energy Saving Mode (ESM) on the basis of a comparison between their channel occupancy and the thresholds. Furthermore, we extend our framework by allowing the thresholds to be dynamically adapted depending on the network performance in terms of blocking probability. We show that the proposed method achieves considerable energy savings when compared to a normal energy-unaware operational mode and still allows to maintain an acceptable level of network performance in terms of blocking probability and end-to-end delay. Numerical results are obtained with a Java event-driven simulator of two different optical network topologies.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".