Stable Logical Topologies for Survivable Traffic Grooming of Scheduled Demands
Bibliographic record
Abstract
There has been considerable research interest in the area of traffic grooming for WDM mesh networks. The vast majority of the current work can be classified into one of two categories, either static grooming or dynamic grooming. In many situations, the individual traffic demands require bandwidth at certain predefined intervals, and resources allocated to nonoverlapping demands can be reused in time. In this paper, we propose a new traffic grooming technique that exploits knowledge of the connection holding times of traffic demands to lead to more efficient resource utilization. We consider wavelength-convertible networks as well as networks without any wavelength conversion capability and implement survivability using dedicated and shared path protection. Although individual demands may be short lived, it is desirable to have a logical topology that is relatively stable and not subject to frequent changes. Therefore, our objective is to design a stable logical topology that can accommodate a collection of low-speed traffic demands with specified setup and teardown times. Our approach results in lower equipment cost and significantly reduced overhead for connection setup/teardown. We present efficient integer linear program (ILP) formulations that address the complete traffic grooming problem, including logical topology design, routing and wavelength assignment, and routing of traffic demands over the selected topology. The primary focus of our ILP formulations is to minimize the resource requirements. However, it is possible to modify our formulations to maximize the throughput, if necessary.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".