UARA in edge routers: an effective approach to user fairness and traffic shaping
Bibliographic record
Abstract
SUMMARY The ever‐increasing share of the peer‐to‐peer (P2P) traffic flowing in the Internet has unleashed new challenges to the quality of service provisioning. Striving to accommodate the rise of P2P traffic or to curb its growth has led to many schemes being proposed: P2P caches, P2P filters, ALTO mechanisms and re‐ECN. In this paper, we propose a scheme named ‘UARA:textbfUser/Application‐awareRED‐basedAQM’ which has a better perspective on the problem: UARA is proposed to be implemented at the edge routers providing real‐time near‐end‐user traffic shaping and congestion avoidance. UARA closes the loopholes exploited by the P2P traffic by bringing under control the P2P users who open and use numerous simultaneous connections. In congestion times, UARA monitors the flows of each user and caps the bandwidth used by ‘power users’ which leads to the fair usage of network resources. While doing so, UARA also prioritizes the real‐time traffic of each user, further enhancing the average user quality of experience (QoE). UARA hence centralizes three important functionalities at the edge routers: (1) congestion avoidance; (2) providing user fairness; (3) prioritizing real‐time traffic. The simulation results indicate that average user QoE is significantly improved in congestion times with UARA at the edge routers. Copyright © 2011 John Wiley & Sons, Ltd.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".