Optimal QoS-based classification for link models with predetermined service levels
Bibliographic record
Abstract
We investigate the problem of optimal QoS- based classification of traffic streams in the context of multi- class link model with predetermined service levels. 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 with arbitrary local QoS, in addition to the bandwidth requirements, to these service levels while achieving the minimum quantization overhead. The quantization overhead is defined as a function of the differences between the required and offered service levels. We formulate the classification as a constrained integer linear optimization problem. We then present two efficient algorithms to obtain the optimal classification for a set of traffic streams for link models with predetermined service levels to minimize the quantization overhead. Our results indicate that by properly selecting the service class weights, the quantization overhead can become as low as 2% using as few as 5 service levels for clustered QoS distribution. On the other hands, if the class weights are not selected appropriately the quantization overhead is around 32% for uniform QoS distribution.
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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 | 0.001 | 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 | 0.001 |
| Open science | 0.004 | 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".