Fairness Guarantees and Achievable QoS in Differentiated Services
Bibliographic record
Abstract
Achievable quality of service (QoS) among flows with disparate metrics in Differentiated Services (Diffserv) presents challenges for network edge traffic conditioners. A fair traffic conditioner was presented by M. El-Gendy and K. Shin in [1]. It provides fairness guarantees by inverting a form of the TCP rate equation as an edge conditioner. It achieves fairness by conditioning all flows regardless of the individual characteristics. We analyzed this conditioner in [2] for effectiveness and provided an enhancement in the area of packet loss rate estimation. We have continued our research and our new contributions are two fold. We present a trend factor in TCP loss rate dynamics and its effect on traffic conditioning. Here, we employed the Holt-Winters algorithm which integrates trend measurement in loss rate estimation. We also investigated the effect of the Time Sliding Window algorithm as a rate estimator on the trend factor and how it affects packet marking. We have analyzed the effectiveness of these two working in tandem in the achievement of fairness in Diffserv.
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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".