Bandwidth Management for Supporting Differentiated Service Aware Traffic Engineering
Bibliographic record
Abstract
This paper presents a bandwidth management framework for the support of differentiated-service-aware traffic engineering (DS-TE) in multiprotocol label switching (MPLS) networks. Our bandwidth management framework contains both bandwidth allocation and preemption mechanisms in which the link bandwidth is managed in two dimensions: class type (CT) and preemption priority. We put forward a Max-Min bandwidth constraint model in which we propose a novel "use it or lend it" strategy. The new model is able to guarantee a minimum bandwidth for each CT without causing resource fragmentation. Furthermore, we design three new bandwidth preemption algorithms for three bandwidth constraint models, respectively. An extensive simulation study is carried out to evaluate the effectiveness of the bandwidth constraint models and preemption algorithms. When compared with the existing constraint models and preemption rules, the proposed Max-Min constraint model and preemption algorithms improve not only bandwidth efficiency, but also robustness and fairness. They achieve significant performance improvement for the well-behaving traffic classes in terms of bandwidth utilization and bandwidth blocking and preemption probability. We also provide guidelines for selecting different DS-TE bandwidth management mechanisms.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".