Dynamic Programming QoS-based Classification for Links with Limited Service Levels
Bibliographic record
Abstract
We investigate the QoS-based classification of traffic streams for a multi-class link model with predetermined service levels. Specifically, we consider a link model with fixed service levels or fixed class weights 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-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 are 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 dynamic-programming problem. We then present a group of polynomial-time-algorithms to obtain the optimal classification for soft and hard QoS requirements. We also present the concept of "differentiation factor" and show the effect of this factor on minimizing the quantization-overhead
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".