Dynamic provisioning of low-speed unicast/multicast traffic demands in mesh-based WDM optical networks
Bibliographic record
Abstract
This paper addresses the problem of dynamically provisioning both low-speed unicast and multicast connection requests in mesh-based wavelength division multiplexing (WDM) optical networks. Several routing/provisioning schemes to dynamically provision both unicast and multicast connection requests are presented. In addition, a constraint-based grooming strategy is devised to utilize the overall network resources as efficiently as possible. Based on this strategy, several different sequential multicast grooming heuristics are first presented. Then, we devise a hybrid grooming approach and combine it with sequential approaches to achieve a grooming scheme that is biased toward serving multicast traffic demands in comparison with all other sequential grooming approaches. To achieve our objective, we decompose the problem into four subproblems: 1) routing problem; 2) light-tree-based logical-topology-design problem; 3) provisioning problem; and 4) traffic-grooming problem. The simulation results of the proposed schemes are compared with each other and with those of conventional nongrooming approaches. To the best of our knowledge, this is the first detailed paper to address and examine the problem of grooming dynamic multicast traffic demands.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".