Fast estimation of probabilities of soft deadline misses in layered software performance models
Bibliographic record
Abstract
Quality of service requirements are normally given in terms of soft deadlines, such as "90% of responses should complete within one second". To estimate the probability of meeting the target delay, one must estimate the distribution of response time, or at least its tail. Exact analytic methods based on state-space analysis suffer from state explosion, and simulation, which is also feasible, is very time consuming. Rapid approximate estimation would be valuable, especially for those cases which do not demand great precision, and which require the exploration of many alternative models.This work adapts layered queueing analysis, which is highly scalable and provides variance estimates as well as mean values, to estimate soft deadline success rates. It evaluates the use of an approximate Gamma distribution fitted to the mean and variance, and its application to examples of software systems. The evaluation finds that, for a definable set of situations, the tail probabilities over 90% are estimated well within a margin of 1% accuracy, which is useful for practical purposes.
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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 | 0.004 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| 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".