A Min-Max Optimization Problem on Traffic Grooming in WDM Optical Networks
Bibliographic record
Abstract
In SONET/WDM networks, a wavelength channel is shared by multiplexed low-rate traffic demands. 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 in one wavelength. Since SADMs are expensive, a key optimization problem in traffic grooming is to minimize the total number of required SADMs to satisfy the full connectivity for a given set of traffic demands. In this paper, we study traffic grooming from a different point of view. We consider a Min-Max optimization problem to minimize the number of SADMs at the network node where the number of required SADMs is the maximum over all nodes. We focus on the unidirectional path-switched ring networks with arbitrary duplex traffic demands. We prove the NP-hardness of this min-max optimization problem, and propose a linear time (k+1/2 + 2)-approximation algorithm. We then show that the approximation algorithm achieves the worst case lower bound. We also study the all-to-all traffic pattern, and propose an algorithm achieving solutions only a constant factor away from the optimal ones. Extensive simulations are conducted as well to validate the performance of our algorithm.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".