Using closed loop feedback control theoretic techniques to improve obs networks performance
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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%.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it