Machine Learning for Regenerator Placement Based on the Features of the Optical Network
Bibliographic record
Abstract
With network traffic projected to increase drastically over the new few years, Elastic Optical Networks (EONs) have been brought in to be the successor of the currently used optical technologies. Many factors must be taken into consideration when deploying EONs for wide-scale use. One of which is the overall network's resource allocation. A simple, uniform distribution of regenerators is too inefficient as different locations have different regenerator requirements based on the amount of network traffic they receive. On the other hand, increasing the number of installed regenerators after initial deployment will incur a substantial cost. The ideal scenario is to accurately predict the number of regenerators that each location will need. One way to provide accurate predictions for regenerator allocation is through the use of machine learning. In order to maximize the accuracy of the prediction provided by the machine learning algorithm, it must be supplied with quality input training data. In this paper, we examine the impact that different network features can have on prediction results. We then propose a list of network features that hold significant impact in regards to predicting regenerator allocation accurately.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".