Maximizing Throughput for Traffic Grooming with Limited Grooming Resources
Bibliographic record
Abstract
In SONET/WDM networks, low-rate traffic demands are usually multiplexed to share a high-speed wavelength channel. The multiplexing/de-multiplexing is known as traffic grooming and 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 a critical optimization problem for traffic grooming is to maximize the number of accommodated traffic demands subject to a given number of SADMs. In this paper, 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 throughput for a given set of traffic demands. 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 case that the given set of traffic demands is the all-to-all set. We propose an algorithm which accommodates at least (nL|radick|)/2 traffic demands, and prove that an optimal solution can accommodate at most nLradick/radic2 traffic demands for the all-to-all set on a UPSR network of n nodes. The solution of our algorithm is at most a constant factor (about radic2) away from the optimal solution.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".