QoS-Based Partitioning and Resource Allocation for Link Models with Variable Service Levels
Bibliographic record
Abstract
We consider the problem of QoS-based partitioning of traffic streams for a link model with adjustable service levels. Specifically, we consider a link model with variable service levels which may be mapped to a finite number of MPLS Label- Switched-Paths (LSPs). Our target is to partition a set of traffic streams each with arbitrary local QoS-demand into a small number of classes and find the service level for each class while optimizing the residual-allocated-resources as a result of the traffic partitioning. The residual allocated resources will be 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 partitioning process as a Dynamic Programming problem. We then present two polynomial time algorithms to obtain the QoSbased optimal partition with bandwidth allocation. Our results indicate that using 4 or 5 service levels will accomplish the tradeoff between complexity and granularity irrespective of the distribution of the QoS requirements.
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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".