The impact of stale information on the blocking performance of dynamic routing, wavelength and timeslot assignment schemes for bandwidth on demand in metro agile all-optical ring networks
Bibliographic record
Abstract
In emerging agile all-optical networks, where time division multiplexing (TDM) in the optical domain is implemented on top of wavelength-division multiplexing (WDM) to improve the network utilization and to support dynamic bandwidth demands, a control mechanism is required to handle the setup and tear down of fast flexible all-optical connections. In this paper, we propose two control protocols for WDM-TDM all-optical ring networks based on whether or not global network information is available. For the global information based protocol, we choose a periodic state-updating mechanism to confine the control message overhead within a reasonable range. For the local information based protocol, we select a backward reservation scheme for dynamic routing, wavelength and timeslot assignment (DRWTA) algorithms to prevent bandwidth overbooking. By conducting extensive numerical simulations, we investigate the impact of imprecise information on the blocking probability of the DRWTA algorithms for the two protocols. Our simulation results show that the local information based protocol outperforms the global information based protocol for metro all-optical networks where propagation delay is small compared to the service time of requests.
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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 | 0.006 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".