Data flow control in ATM networks: an evaluation of ER and BCA
Bibliographic record
Abstract
Data traffic in modern telecommunications systems is loss-sensitive, delay-insensitive, and highly bursty. Most data applications cannot predict their own traffic parameters, but require low cell loss rates. These features make traffic management for data traffic complex. In ATM networks, where multiple types of applications co-exist, it becomes even more difficult. Many models have been proposed for the flow control of data traffic over ATM networks. Among them the explicit rate (ER) mechanism proposed for the available bit rate (ABR) service is the principal method. The ER and other proposed mechanisms are found to suffer in some regard. These mechanisms also need many buffers in the intermediate switches and thus increase the hardware costs. Flow control based on bandwidth contracting (BCA), can achieve higher bandwidth throughputs at lower hardware costs. In this paper, we evaluate ER and BCA mechanisms in LAN and WAN environments for bursty data traffic. This study focuses on the use of short-term bandwidth contracts for the reliable transport of bursty data.
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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 | 0.011 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".