Stochastic qos-based classification for link models with calculated service levels
Bibliographic record
Abstract
We investigate the problem of stochastic-QoS-based-classification of traffic streams for a multi-class-link-model with predetermined service levels calculated based on the link's total load. Specifically, we consider a link model with fixed service levels which may be represented by a finite number of MPLS-label-switched-paths (LSPs). Our target is to classify a set of traffic streams each with arbitrary local QoS requirement, in addition to the bandwidth demand into a small number of service-levels while optimizing the residual-allocated-resources as a result of the traffic classification. The residual-allocated-resources is measured by the service-quantization-overhead which is the summation of the differences between the required QoS and the offered service-level for all traffic streams. We formulate the classification as a constrained integer-linear optimization problem. We then present two efficient algorithms based on branch and bound technique to obtain the optimal classification for a set of traffic streams for link models with predetermined service levels.
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How this classification was reachedexpand
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".