Improving BGP Convergence And Reachability Through Stable Path Aggregation (SPAGG)
Bibliographic record
Abstract
BGP is the standard inter-domain routing protocol of the internet. It has proven to be scalable enough to accommodate the exceptional growth of the Internet. However, because of the sheer size of the Internet and the complexity of its topology, the behaviour of BGP can be unpredictable sometimes. Researchers have been proposing various changes and enhancements in the past 10 to 15 years to improve the security, stability and convergence of BGP. Some of the solutions have been adopted, but BGP is still suffering from possible deficiencies when it comes to convergence time and stability at specific situations and scenarios. In this thesis, we focus on providing a reasonable solution for the problem of BGP instability but without causing long convergence, which leads eventually into minimizing BGP churn and path exploration. We, first, analyse the current BGP standard protocol and previous proposed solutions. Then, we study current problems associated with a recently proposed improvement, suggest a new algorithm that avoids path selection problem at the aggregator and the path shortening problem. We also describe its implementation in OPNET. Finally, we show the results from our simulation and compare them to the results of previous work suggested. Our results show a great improvement of the convergence of BGP while preserving reachability and optimality all the time.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".