A Short-Time Burst degradation Classifier for Real-Time Traffic with Application in MPLS Ingress Nodes
Bibliographic record
Abstract
In this paper, we propose a novel classifier technique, named short-time burst degradation classifier (SBDC), to improve the short-term delay and the packet jitter for real-time traffic. In the classifier, we manage the traffic to improve the sort-term QoS provisioning in a flexible manner, since traditional mechanisms such as leaky bucket can not have such kind of flexibility. Even though, the proposed scheme is general and can be used in different points of the network, we propose to use it in MPLS ingress nodes. To evaluate the performance of the classifier we propose an efficient scheduler, called short-term quality-of-service class based queuing (SQ-CBQ), to be combined with our classifier. The scheduler uses a new scheduling algorithm named polling deficit round robin (PDRR). Also, after using the combination of the classifier and the scheduler in an MPLS ingress node the impact of short-time scale burstiness of the traffic will be decreased. The performance analysis shows that high quality of service provisioning for the real-time traffic will be achieved.
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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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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 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".