An enhanced algorithm for fair traffic conditioning in differentiated services networks
Bibliographic record
Abstract
Fair bandwidth sharing among traffic flows with different characteristics in Differentiated Service (DiffServ) networks is the focus of the current research. This paper examines and enhances an algorithm developed to enforce fairness among disparate TCP flows in the assured forwarding (AF) service in DiffServ. equation based marking (EBM) was introduced (M. El-Gendy and K. Shin (2002)) to enforce fairness in AF by monitoring existing network conditions used in marking decisions. The estimation of packet losses by the algorithm is integral to marking. The loss rates of different connections were demonstrated to converge hence enforcing a fair marking regardless of the metrics of individual flows. In this paper, EBM is analyzed for fairness and enhanced by implementing a more efficient technique for loss rate estimation. Comparison is made between EBM and the enhanced technique with results showing appreciable improvements in the maintenance of fairness. Furthermore, a service definition required by QoS standards is met with the implementation of the additional algorithm to EBM.
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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 | 0.001 |
| 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".