Effect of MRAI timers and routing policies on BGP convergence times
Bibliographic record
Abstract
The Minimal Route Advertisement Interval (MRAI) plays a prominent role in convergence of the Border Gateway Protocol (BGP). Previous studies have suggested using adaptive MRAI and reusable timers to reduce the BGP convergence time. The adaptive MRAI timers perform well under the normal load of BGP updates. However, a large number of BGP updates may flood Internet routers. We propose a new algorithm, MRAI with Flexible Load Dispersing (FLD-MRAI), which reduces the router's overhead by dispersing the load in case of a large number of BGP updates. We also examine the MRAI timers under the normal load of BGP updates. Since BGP routing policies play a significant role in preserving the Internet routing stability, we evaluate their impact on BGP convergence time and Route Flap Damping (RFD) algorithms. The proposed algorithms are evaluated using the ns-BGP network simulator.
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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 | 0.005 | 0.039 |
| 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 | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".