Experimental study of router buffer sizing
Bibliographic record
Abstract
During the past four years, several papers have proposed rules for sizing buffers in Internet core routers. Appenzeller et al. suggest that a link needs a buffer of size O(C/√N), where C is the capacity of the link, and N is the number of flows sharing the link. If correct, buffers could be reduced by 99% in a typical backbone router today without loss in throughput. Enachecsu et al., and Raina et al. suggest that buffers can be reduced even further to 20-50 packets if we are willing to sacrifice a fraction of link capacities, and if there is a large ratio between the speed of core and access links. If correct, this is a five orders of magnitude reduction in buffer sizes. Each proposal is based on theoretical analysis and validated using simulations. Given the potential benefits (and the risk of getting it wrong!) it is worth asking if these results hold in real operational networks. In this paper, we report buffer-sizing experiments performed on real networks - either laboratory networks with commercial routers as well as customized switching and monitoring equipment (UW Madison, Sprint ATL, and University of Toronto), or operational backbone networks (Level 3 Communications backbone network, Internet2, and Stanford). The good news: Subject to the limited scenarios we can create, the buffer sizing results appear to hold. While we are confident that the O(C/√N) will hold quite generally for backbone routers, the 20-50 packet rule should be applied with extra caution to ensure that network components satisfy the underlying assumptions.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".