A novel distributed progressive reservation protocol for WDM all-optical networks
Bibliographic record
Abstract
In this paper, we propose and describe a new distributed reservation protocol for establishing lightpaths in WDM all-optical networks. Distributed control mechanisms are preferred and employed because of their advantages over centralized ones to set up virtual channels. The new protocol is a combination of the conservative and aggressive backward reservation protocols, which attempts to improve performance by adapting a reservation to network circumstances. On the one hand, the new protocol uses network circumstances and decides and applies a more conservative or aggressive approach. In other words, the protocol progressively fluctuates between those reservation protocols in order to capture their respective advantages. As a result, in extreme cases it acts exactly like either the conservative or the the aggressive reservation protocol. On the other hand, it considers the characteristics of a network to set a retry-list size. As a result, a retry-list size is not determined by a fixed number but modified based on the multiplexing degree of a network, which prevents imposing ineffective retries on a network with a small number of wavelengths and instead encourages more retries for a network with numerous channels. Therefore, the proposed protocol transforms the static nature of existing reservation protocols into a more adaptive one in order to enhance network performance.
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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 | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".