A Feedback Control Model for Multiple-Link Adaptive Bandwidth Provisioning Systems
Bibliographic record
Abstract
Future IP networks are required to support services with different and distinct end-to-end Quality of Service (QoS) requirements. Adaptive bandwidth provisioning serves as an attractive solution for providing guaranteed distinctive QoS and maintaining high network efficiency at the same time. However, most previous research on adaptive bandwidth provisioning is limited to the single-link case and assumes dedicated bandwidth. This paper studies multiple-link adaptive bandwidth provisioning for end-to-end statistical QoS guarantee in bandwidth sharing networks with Generalized Processor Sharing (GPS) schedulers. A feedback control model for end-to-end multiple-link adaptive bandwidth provisioning systems is presented. This model is verified by simulations, and it is shown to match the actual dynamics of adaptive bandwidth provisioning systems well. Based on this feedback control model, different controllers are designed and analyzed using control theory, and their performances are compared. The analysis and simulations show that the proposed end-to-end multiple-link bandwidth provisioning scheme is able to provide guaranteed end-to-end statistical QoS, and that both the adaptive P controller and adaptive PI controller can achieve better performance than the simple non-adaptive P controller.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".