Bibliographic record
Abstract
In synchronous optical networks (SONETs) and WDM networks, low-rate traffic demands are usually multiplexed to share a high-speed wavelength channel. The multiplexing-demultiplexing is known as traffic grooming and is performed by SONET add-drop multiplexers (SADM). The grooming factor, denoted by k, is the maximum number of low-rate traffic demands that can be multiplexed into one wavelength channel. SADMs are expensive, and thus an important optimization problem for traffic grooming is to maximize the number of accommodated traffic demands subject to a given number of SADMs. We focus on the unidirectional path-switched ring (UPSR) networks with unitary duplex traffic demands. We assume that each network node is equipped with a limited number L of SADMs, and our objective is to maximize the number of accommodated traffic demands in a given set. We prove the NP-hardness of this maximum throughput traffic grooming problem and propose a (k+1)-approximation algorithm. Extensive simulations are conducted to validate the performance of the algorithm. We also study the all-to-all traffic pattern for the maximum throughput traffic grooming problem and propose an algorithm that accommodates at least (nL⌊k⌋)/2 demands for a UPSR with n nodes. We also prove that any optimal solution can accommodate at most (nLk)/2 demands. Thus the solution of our algorithm is at most a constant factor (approximately 2) away from the optimum.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".