A scalable cluster distributed BGP architecture for next generation routers
Bibliographic record
Abstract
In classical monolithic router architecture, the Border Gateway Protocol (BGP) engine is implemented as a multiprocess centralized function within the controller entity. This architecture does not scale well and its performance decreases when the load increases, forcing multiple processes to compete for the same controller processor. In addition only a limited number of peer connections can be handled. In this paper, we propose a novel scalable distributed architecture for the BGP engine without modifying the core of the BGP protocol as defined in RFC 4271. The proposed architecture is designed according to the ¿Master-Slave¿ task separation along with the replication of the Routing Information Base (RIB) on multiple controller cards. In addition, we design a new consistency algorithm for the RIB replication. Simulations show an acceptable trade-off between the scalability to a large number of peer sessions and the overhead caused by the communication latency. Furthermore, simulations show that the proposed architecture handily outperforms the actual BGP processing capacity. Accordingly, we conclude that our approach increases considerably both scalability and reliability thanks to the replication of the RIB.
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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 | 0.001 | 0.001 |
| 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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".