Prioritized e‐mail servicing to reduce non‐spam delay and loss: A performance analysis
Bibliographic record
Abstract
Abstract This paper proposes a prioritized e‐mail servicing on e‐mail servers to reduce the delay and loss of non‐spam e‐mails due to queuing. Using a prioritized two‐queue scheme, non‐spam e‐mails are queued in a fast queue and given higher service priority than spam e‐mails that are queued in a slow queue. Four prioritized e‐mail service strategies for the two‐queue scheme are proposed and analyzed. We modeled these four strategies using discrete‐time Markov chain analysis under different e‐mail traffic loads and service capacities. Non‐spam e‐mails can be delivered within a small delay, even under heavy e‐mail loadings and high spam‐to‐non‐spam a priori. Results from our analysis of the two‐queue scheme show that it gives non‐spam delay and loss probability two orders of magnitude smaller than the typical single‐queue approach during heavy spam traffic. Moreover, prioritized e‐mail servicing protects e‐mail servers from spam attacks. Copyright © 2007 John Wiley & Sons, Ltd.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".