A model of BGP routing for network engineering
Why this work is in the frame
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Bibliographic record
Abstract
The performance of IP networks depends on a wide variety of dynamic conditions. Traffic shifts, equipment failures, planned maintenance, and topology changes in other parts of the Internet can all degrade performance. To maintain good performance, network operators must continually reconfigure the routing protocols. Operators configure BGP to control how traffic flows to neighboring Autonomous Systems (ASes), as well as how traffic traverses their networks. However, because BGP route selection is distributed, indirectly controlled by configurable policies, and influenced by complex interactions with intradomain routing protocols, operators cannot predict how a particular BGP configuration would behave in practice. To avoid inadvertently degrading network performance, operators need to evaluate the effects of configuration changes before deploying them on a live network . We propose an algorithm that computes the outcome of the BGP route selection process for each router in a single AS, given only a static snapshot of the network state, without simulating the complex details of BGP message passing. We describe a BGP emulator based on this algorithm; the emulator exploits the unique characteristics of routing data to reduce computational overhead. Using data from a large ISP, we show that the emulator correctly computes BGP routing decisions and has a running time that is acceptable for many tasks, such as traffic engineering and capacity planning.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it