Cost and performance optimization in IP switched-routers
Bibliographic record
Abstract
The explosive growth of Internet users, the increased user demand for bandwidth, and the declining cost of technology, has all resulted in the emergence of new classes of high-speed distributed IP router architectures with packet forwarding rates on the order of gigabits or even terabits per second. This paper develops an analytical framework for modeling and analyzing the impact of technological factors on the cost-performance trade-offs in distributed router architectures. The main trade-off in a distributed router results naturally from moving the main packet forwarding and processing power from a centralized forwarding engine to an ensemble of smaller forwarding engines either dedicated to or shared among the line cards. Processing packets in these smaller engines can be much cheaper (by as much two to three orders of magnitude) than in a centralized forwarding engine. Therefore, the main goal of our modeling framework is to determine an optimal allocation of processing power to the forwarding engines (in a distributed router) to minimize overall router cost while achieving a given level of packet forwarding performance. Two types of router models are analyzed using the proposed framework: a distributed router architecture and parallel router architecture.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".