Aggregate flow control: improving assurances for differentiated services network
Bibliographic record
Abstract
The differentiated services architecture is a simple, but novel, approach for providing service differentiation in an IP network. However, there are various issues to be addressed before any sophisticated end-to-end services can be offered. This work proposes an aggregate flow control (AFC) technique with a Diffserv traffic conditioner to improve the bandwidth and delay assurance of differentiated services. A prototype has been developed to study the end-to-end behavior of customer aggregates. In particular, this new approach improves performance in the following manner: (1) fairness issues among aggregated customer traffic with different number of micro-flows in an aggregate, interaction of non-responsive traffic (UDP) and responsive traffic (TCP), and the effect of different packet sizes in aggregates; (2) improved transactions per second for short TCP flows; and (3) reduced inter-packet delay variation for streaming UDP traffic. Experiments are also performed in a topology with multiple congestion points to show an improved treatment of conformant aggregates, and the ability of AFC to handle multiple aggregates and differing target rates.
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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 | 0.003 | 0.007 |
| 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 | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".