Cell discarding policies supporting multiple delay and loss requirements in ATM networks
Bibliographic record
Abstract
Future ATM networks will carry a wide range of applications which could differ significantly in their delay and loss requirements. In such an environment, supporting multiple delay requirements as well as loss requirements becomes indispensable to the priority mechanisms such as cell discarding policies employed in ATM switches. Traditional cell discarding policies, such as last-in-first-out (LIFO), pushout, threshold, and QoS schemes, cannot support multiple delay bounds efficiently. In this paper, we generalize traditional schemes and propose four new policies, termed G-LIFO, G-pushout, G-threshold and G-QoS scheme, respectively. We show that when coupled with the earliest-deadline-first service scheduling discipline these four new policies can support multiple delay and loss requirements more efficiently than their conventional counterparts. We prove that the G-QoS scheme is optimal in terms of efficiency among all generalized space-conserving, stable schemes and also optimal among all stable schemes if all traffic flows are equally demanding. Simulation studies are conducted to examine the performance of the proposed cell discarding policies.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".