Burst loss reduction schemes in optical burst switching networks
Bibliographic record
Abstract
Burst loss is the major problem that faces the development of optical burst switching (OBS) networks. The burst loss is caused by the contention which affects badly OBS performance, especially under a high load. In this paper we propose two schemes: anticipated retransmission (called AR) and admission control (called QAC). AR aims at reducing the burst loss. The basic idea behind AR is to anticipate retransmission of dropped bursts by sending systematically two copies (primary and secondary) of each burst over two different paths. The traffic composed of the secondary bursts has lower priority and does not interfere/contend with the primary traffic. QAC aims at reducing the burst loss inside the network, especially in highly loaded network. The basic idea behind QAC is to block bursts before entering the network in a way to bound the burst loss inside the network. Applying AR to bursts not blocked by QAC reduces further the burst loss. The simulation results show that AR reduces considerably the burst loss in moderately loaded networks and QAC (combined with AR) guarantees an upper bound for burst losses inside the network.
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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) | 0.001 | 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 | 0.000 | 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".