Improved Chain Calculation for Sub-chain Dependencies in Layered Queueing Networks
Bibliographic record
Abstract
Often, many software systems fail to meet requirements because of a lack of performance. A proven method for preventing or for diagnosing performance problems is through modeling. Layered Queueing Networks (LQN) are one popular technique for solving performance models. However, if a LQN is solved through decomposition and Mean Value Analysis (MVA), erroneous results can arise because of traffic dependencies in the decomposed models. This paper addresses one traffic dependency, called sub-chains, where customers from one chain "bleed into" another chain causing "extraneous" queueing delays. The new approach described here changes approximate MVA by adjusting the population in a routing chain depending on the originating sub-chain. This new approach substantially reduces, or even eliminates, the extraneous queueing delay caused by the sub-chain dependent traffic. The approach was applied to a substantial model of an on-line bookstore, and reduced the overall error in queueing time by a factor of 20 times, when compared to simulation. The more accurate queueing estimates yield better results for the other outputs of the LQN solver.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".