The influence of low-class traffic load on high-class performance and isolation in optical burst switching systems
Bibliographic record
Abstract
In optical burst switching (OBS) networks, class differentiation and isolation can be achieved by assigning adequately long time offsets between the control packet and payload of high-class bursts. While it has been recognized that the length distribution of low-class bursts plays a role in determining the size of the offsets required, there have been no studies on the effect of other factors that may also be significant. In this paper we examine the effect of the ratio of the arrival rates of low-class and high-class traffic on the level of isolation achieved in OBS networks with quality of service offsets. We show that the level of isolation in the network depends on the arrival rate of low-class traffic, especially when the amount of low-class and high-class traffic in the system is comparable. When we vary the ratio between low and high-class arrival rates from 0.1 to 10, an additional offset of three times the mean low-class burst length is required to achieve the same level of isolation. These results imply that it is important for researchers and network designers to take into account the amount of low-class traffic in the network when provisioning offsets for class differentiation in OBS networks.
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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 | 0.005 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".