Bibliographic record
Abstract
The Border Gateway Protocol (BGP) is the de facto inter-domain routing protocol used to exchange reachability information between autonomous systems in the global Internet. The BGP is a path vector routing protocol. Distance vector routing protocols can take a long time to converge after a topological change. It is believed that the adoption of the path vector solves this problem. One of the objectives of this thesis is to investigate this claim. The BGP specification lacks convergence behavioral and performance analysis. This thesis presents the analysis of the BGP convergence behavior and performance. The behavior of the protocol can be estimated in an experimental manner by means of simulations. The effect of network topology on the number of BGP routing updates and convergence latency is examined. The analysis in this thesis is based on data collected in a simulation environment. The best and the worst-case of BGP convergence models are simulated. This analysis shows that BGP has bouncing problem. In the case of a route failure event, the upper bound on volume of routing update messages is found to be factorial and convergence latency is linear with respect to the number of autonomous systems. In the case of a route announcement event, the upper bound on number of routing update messages is found to be exponential with respect to the number of autonomous systems. It is found that performing MinRouteAdvertisementlnterval timer and loop detection on the receiver router significantly reduces the number of BGP routing updates.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".