Using Ethernet Commodity Switches to Build a Switch Fabric in Routers
Bibliographic record
Abstract
Switch fabric in routers requires very tight characteristics in term of packet loss, fairness in bandwidth allocation, no head-of-line blocking and low latency. Such attributes are traditionally resolved using specialized and expensive switch devices. Motivated by the emergence of IEEE Data Center Bridging, we explore the possibility of using commodity Ethernet switches to achieve scalable, flexible, and more cost efficient solutions, while still guaranteeing the switch characteristics. In this context, we propose Ethernet Congestion Control & Prevention (ECCP), a novel concept to control and prevent congestion in switch fabrics. ECCP consists of (1) a method to estimate the available bandwidth along a given network path using a train of probes and (2) a rate control algorithm to adjust the sending rate of traffic along this path based on the estimated bandwidth. To prove ECCP and evaluate its characteristics, we present a first prototype based on the OMNEST simulator and conduct extensive experiments. Our analysis confirms that ECCP is a viable solution to (1) avoid congestion within the fabric, thus minimizing path latency and avoiding packet loss, (2) guarantee fair share of the link capacity between flows, and (3) avoid head of line blocking.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".