Statistical Delay Budget Partitioning Algorithm
Bibliographic record
Abstract
Mapping the end to end QoS requirements into link QoS requirements is an important step for resource allocation of connection oriented services. The problem of the QoS partitioning has been addressed in literature and proved to be NP complete. Different algorithms are proposed to solve the problem of single end-to-end QoS metric. However, these algorithms are near optimal or heuristic algorithms and solve the QoS partitioning problem for single QoS metric. In this paper, we propose a novel optimal partitioning algorithm which is capable of partitioning the end to end QoS requirement for multiple QoS metrics, additive and multiplicative, simultaneously. Extensive simulation verified the effectiveness of the algorithm compared to two QoS partitioning algorithms. The results show that the proposed algorithm outperforms the other two algorithms for loose and stringent QoS requirements and over different path lengths.
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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 | 0.001 | 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".