A Bandwidth Bargain Model based on Adaptive Weighted Fair Queueing
Bibliographic record
Abstract
Efficient usage of network bandwidth is a key factor of providing quality of service guarantees in the Internet. In this paper, a bandwidth bargain model is developed which aims to dynamically allocate network bandwidth based on the varying demand of packet flows. To achieve the design goal, an adaptive weighted fair queueing (AWFQ) algorithm is presented which is more flexible than the generic weighted fair queueing (WFQ). By using the estimation of flow arrival rate and weight adjustment approach, AWFQ has the ability to guarantee bandwidth requirements of active service within a pre-defined range, without compromising the guarantee to assured service. Furthermore, AWFQ can provide a specified minimum bandwidth to best-effort service, which cannot be offered by WFQ, when the amount of competing traffic exceeds the link capacity. The simulation results, which are obtained from different network topologies with various packet generating processes, verify that AWFQ is feasible and performs better than WFQ in the context of dynamic bandwidth allocation
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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".