A Bandwidth Allocation Scheme in Optical TDM Network
Bibliographic record
Abstract
Wavelength Routing (WR) and Optical Burst Switching (OBS) are two optical network techniques that have received enormous attention over the last decade. However, the two techniques are plagued with many problems. The main concern with WR is the inefficient bandwidth utilization. On the other hand, the problem with OBS is resource contention and burst dropping. In this paper, we propose a new scheme to share network resources using Time Division Multiplexing (TDM) instead of the statistical multiplexing employed in optical burst switching. To avoid contention and improve bandwidth utilization, we resort to a simple reservation scheme that guarantees timeslot deliveries. In addition, we propose the deployment of a new device that we call Sequencer, a simplified form of Optical Time Slot Interchangers (OTSIs), to assist in mapping incoming timeslots to some available outgoing ones. Our goal is to achieve a contention free network, and improve performance. Many classes of traffic can coexist in our network by adjusting the bandwidth allocation parameters. 1.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".