End-to-end flow control in interconnected local area ring networks
Bibliographic record
Abstract
The authors evaluate the performance of end-to-end flow control in a system of interconnected token-ring local area networks via a backbone ring network. The analysis is based upon an existing delay approximation for a stand-alone local area network. This approximation is extended to analyze a system of interconnected local area ring networks. Local area networks are interconnected via bridges. A bridge is modeled as two queues: one queue is for the backbone ring and the other is for the individual ring or subnetwork. Average delay, throughput, and power are used as performance measures. Traffic is assumed to be balanced. Bridges are assumed to have enough buffers such that messages are not blocked. The probability of transmission errors is assumed to be negligible. Analytical results show that tighter restrictions result in poor utilization of resources although the performance in terms of delay may look very attractive. On the other hand, loose restrictions (i.e. larger window size) mean no flow control and result in performance degradation under heavy traffic conditions. Therefore, it is suggested that window size should be dynamically adjusted according to the traffic conditions in order to achieve the best performance.>
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".