Using closed loop feedback control theoretic techniques to improve obs networks performance
Bibliographic record
Abstract
This paper considers the use of closed loop feedback control theoretic techniques to improve the performance of Optical Burst Switching (OBS) networks. In OBS networks, the Burst Loss Ratio (BLR) is the ratio between the lost bursts to the sent bursts. The BLR is used as a performance metric. The desired burst loss ratio depends on the application using the network. Some applications might tolerate more burst loss ratios than other applications. Higher network link utilization could be achieved by having more control over the burst loss ratio. Burstification rate is the rate of injecting bursts into the OBS network. In this paper, a novel technique to control the burst loss ratio in OBS networks is proposed. The technique is based on classical control theory approaches to tune the burstification rate in order to achieve a desired burst loss ratio to satisfy the application requirements. Extensive experiments show that the proposed technique achieves promising results. That is, the measured burst loss ratio hovers around the desired burst loss ratio and higher utilization is observed. Empirical approaches are used to identify the proposed model. The empirical model fits the OBS network by a value that did not fall below 75%.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".