Separating resource reservations from service requests to improve the performance of optical burst-switching networks
Bibliographic record
Abstract
In this paper, we introduce a new signalling architecture called Dual-header Optical Burst Switching (DOBS) for next generation burst-switching optical networks. DOBS decouples the resource reservation process from the service request process in core nodes and allows for delayed scheduling to be implemented. This relaxes the constraints on burst scheduling operations and allows the offset sizes of bursts to be precisely controlled in core nodes without the use of fiber delay line buffers. This allows for increased flexibility, control, and performance. To demonstrate the benefit of delayed scheduling and core. node offset control, we examine the performance of a DOBS system in which the offset size of every burst on a core link is set to a constant value. Using simulation and analysis, we show that the resulting constant-scheduling-offset (CSO) system realizes lower ingress delay, higher throughput, and better fairness than conventional single-header OBS systems, while simultaneously requiring only O(1) burst scheduling complexity. In a 16-channel system with full wavelength conversion and no fiber delay line buffers, the CSO DOBS system achieved a blocking probability 50% lower than that of a similar LAUC-VF JET OBS system. The CSO DOBS system also achieved perfect fairness, both with respect to burst length and with respect to the residual path length of bursts
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".