Dynamic admission control and path allocation for SLAs in DiffServ networks
Bibliographic record
Abstract
Today's converged networks are mainly characterized by their support of real-time and high priority traffic requiring a certain level of quality of service (QoS). In this context, traffic classification and prioritization are key features in providing preferential treatments of the traffic in the core of the network. In this paper, we address the joint problem of path allocation and admission control (JPAC) of new Service Level Agreements (SLA) in a DiffServ domain. In order to maximize the resources utilization and the number of admitted SLAs in the network, we consider a statistical bandwidth constraints allowing for a certain overbooking over the network's links. SLAs' admissibility decisions are based on solving to optimality an integer linear programming (ILP) model. When tested by simulations, numerical results confirm that the proposed model can be solved to optimality for real-sized instances within acceptable computation times and substantially reduces the SLAs blocking probability, compared to a the Greedy mechanism proposed in the literature.
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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".