Caliper: Precise and Responsive Traffic Generator
Bibliographic record
Abstract
This paper presents Caliper, a highly-accurate packet injection tool that generates precise and responsive traffic. Caliper takes live packets generated on a host computer and transmits them onto a gigabit Ethernet network with precise inter-transmission times. Existing software traffic generators rely on generic Network Interface Cards which, as we demonstrate, do not provide high-precision timing guarantees. Hence, performing valid and convincing experiments becomes difficult or impossible in the context of time-sensitive network experiments. Our evaluations show that Caliper is able to reproduce packet inter-transmission times from a given arbitrary distribution while capturing the closed-loop feedback of TCP sources. Specifically, we demonstrate that Caliper provides three orders of magnitude better precision compared to commodity NIC: with requested traffic rates up to the line rate, Caliper incurs an error of 8 ns or less in packet transmission times. Furthermore, we explore Caliper's ability to integrate with existing network simulators to project simulated traffic characteristics into a real network environment. Caliper is freely available online.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".