A minimum-work weighted fair queuing algorithm for guaranteed end-to-end and quality-of-service in packet networks
Bibliographic record
Abstract
Emerging applications in multimedia communications and Virtual Private Networks (VPNs) require data networks to provide Quality-of-Service (QoS) guarantees, such as delay and/or jitter bounds, to individual packet flows. Providing such guarantees can be achieved by link scheduling mechanisms along the path of these packets. Among the many packet-scheduling techniques proposed for this problem, Weighted Fair Queuing (WFQ) offers the best delay and fairness guarantees. However, all previous work on WFQ has been focused on developing inefficient approximations of the scheduler because of perceived scalability problems in the WFQ computation. This thesis proves that the previously well accepted O(N) time-complexity for WFQ implementation, where N is the number of active flows handled by the scheduler, is not true. The other key contribution of the thesis is a novel Minimum- Work Weighted Fair Queuing (MW-WFQ) algorithm which is an 0(1) algorithm for implementing WFQ. In addition, the thesis presents several performance studies demonstrating the power of the proposed algorithm in providing precise delay bounds to a large number of sessions with diverse QoS requirements, whereas other well known scheduling techniques have failed to provide the same guarantees for the same set of sessions.
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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 | 0.002 | 0.003 |
| 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 | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".