Bibliographic record
Abstract
Next generation (NG) routers, characterized by high speed interfaces, large switching capacity and petabit packet processing speed have recently been deployed in core networks of world class operators. Based on distributed architectures, these routers are designed with control cards and line cards interconnected by a very high-speed switch fabric, where line cards contain processing and memory resource allowing the sharing of some route processing tasks with control cards. The traditional implementation model of router software, where control cards assume all the processing tasks, is therefore no longer appropriate. In this paper, we propose a distributed model for implementing router software in order to fully exploit the hardware platform of the next router generation, taking into account the additional capacity of line cards. The model corresponds to a distributed architecture with control cards acting as super nodes and line cards acting as peers. It also provides "direct" communication between line cards, allowing them to cooperate in some task processing without going through control cards. Such a model significantly increases the robustness, scalability and availability of routers. We also investigate the proposed distributed model in the context of different protocols supported by a router, such as signaling and routing protocols. Two case studies are presented where we discuss the advantages of the distributed model for OSPF and LDP protocols.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".