A New Approximation for Multiserver Waiting Time, for Layered Queueing Systems
Bibliographic record
Abstract
Performance models must solve quickly even for large systems, to be useful in searching for changes that might give improvements.Thus, an efficient solution is important, and is required by the Layered Queueing Network Solver (LQNS).This thesis introduces two approximations based on the queue states by binomial probabilities, to estimate the waiting time for multiservers (such as multi-threaded tasks or multi-core CPUs).Accuracy and speed of convergence were evaluated for one class and for multiple classes of customers.The binomial approximations are compared with the "Rolia-Franks" approximation which is a relatively fast approximation that is currently used in the LQNS model-solving tool.One of the approximations (called the "Arrival-Theorem Binomial", or AB) is better, with smaller errors in most cases.A novel approach for dealing with multiple classes is also evaluated, and a case study is included in which the AB approximation is combined with an LQNS solution.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 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) | 0.003 | 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".