A strict priority scheme for quality-of-service provisioning in optical burst switching networks
Bibliographic record
Abstract
Classical approaches to quality-of-service (QoS) provisioning in IP networks are difficult to apply in all-optical networks. This is mainly because there is no optical counterpart to the store-and-forward model that mandates the use of buffers for queuing packets during contention for bandwidth in electronic packet switches. Since plain IP assumes a best effort service model, there is a need to devise mechanisms for QoS provisioning in IP over wavelength-division-multiplexing, or IP-over-WDM, networks. In this paper, we propose a new scheme; called preemptive prioritized just enough time (PPJET), for QoS provisioning in buffer-less optical burst switching (OBS) networks. PPJET provides strict priority for high priority traffic by dropping reservations belonging to lower priority traffic using a new channel scheduling algorithm called preemptive latest available unused channel with void filling (PLAUC-VF). Furthermore, we study the performance of PPJET through simulation experiments, and we show that PPJET outperforms prioritized just enough time (PJET) in terms of dropping probability and end-to-end delay.
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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 | 0.002 | 0.006 |
| 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 | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".