On the Impact of Link Scheduling on End-to-End Delays in Large Networks
Bibliographic record
Abstract
We seek to provide an analytical answer whether the impact of link scheduling algorithms on end-to-end delays diminishes on long network paths. The answer is provided through a detailed multi-hop delay analysis, which is applicable to a broad class of scheduling algorithms, and which can account for statistical multiplexing. The analysis is enabled by two contributions: (1) We derive a function that can characterize the available bandwidth at a buffered link for various scheduling algorithms. This characterization is sharp enough to provide necessary and sufficient conditions for satisfying worst-case delay bounds at a single link; (2) We obtain end-to-end delay bounds by solving an optimization problem, in which the service received on a multi-hop path is subsumed into a single function. Since our analysis captures the properties of a broad group of schedulers in a single parameter, it can provide insight how the choice of scheduling algorithms impacts end-to-end delay bounds. An important finding of this paper is that schedulers may exhibit noticeable performance differences which persist in a network setting with long paths.
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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 | 0.007 | 0.061 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".