Packet-loss-robust load-balancing switch with distributed extended cross-point queues
Bibliographic record
Abstract
A packet switch architecture and a method for load-balancing are described, which involve no centralised schedulers. The method is implemented by distributing extended cross-point queues (a three-dimensional structure) over all elements of the switch and deploying pollers to append packets and to select the queues to be served, together with simple local work-conserving schedulers. The queue structure is such that it renders the proof that no packet will be mis-sequenced trivial. The architecture is practical and shows enhanced performance compared with other state-of-the-art load-balancing architectures, not only for the average delay but also for the distribution of individual delays, the latter being measured by a custom tool that compares the performance of the architecture to the ideal operation of an output queued switch. The queue structure permits the fair penalisation of only the offending input–output flows within the switch in the case of buffer overflow. The basic scheme is enhanced to avoid improper operation in the presence of packet drops, a problem that reintroduces mis-sequencing and that has not been properly addressed in the class of architectures that use pollers to distribute packets.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 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".