Author manuscript, published in "ACM SIGCOMM, Canada (2011)" Predicting and Tracking Internet Path Changes
Bibliographic record
Abstract
This paper investigates to what extent it is possible to use traceroute-style probing for accurately tracking Internet path changes. When the number of paths is large, the usual traceroute based approach misses many path changes because it probes all paths equally. Based on empirical observations, we argue that monitors can optimize probing according to the likelihood of path changes. We design a simple predictor of path changes using a nearestneighbor model. Although predicting path changes is not very accurate, we show that it can be used to improve probe targeting. Our path tracking method, called DTRACK, detects up to two times more path changes than traditional probing, with lower detection delay, as well as providing complete load-balancer information. Categories and Subject Descriptors
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.406 | 0.211 |
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".