Deriving distribution of thread service time in layered queueing networks
Bibliographic record
Abstract
Replication is a technique used in distributed systems to improve performance, availability, and reliability. In replication schemes, often a J out of $N$ voting pattern (also called quorum) is used in which the quorum waits for J replies to arrive. Integrating a quorum scheme into the Layered Queueing Network (LQN) performance modeling language necessitates the computation of the quorum response time as the Jth order statistic. To do so, we need the exact (or an accurate estimation of the) time distribution of individual replies. This distribution was estimated in previous work but only for the special case of (J=N) and yields large errors for J « N. This paper presents a new analytic approach for the derivation of the distributions. Under a number of assumptions, we derive closed form expressions for the probability distribution functions of the replies. The application of our new approach on a number of LQN models shows that, even for models that violate those assumptions, it is far more accurate than previous approaches and it yields an error less than 10% for most example models.
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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 | 0.005 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".