Multiclass multiservers with deferred operations in layered queueing networks, with software system applications
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Layered queueing networks describe the simultaneous-resource behaviour of servers that request lower-layer services and wait for them to complete. Layered software systems often follow this model, with messages to request service and receive the results. Their performance has been computed successfully using mean-value queueing approximations. Such systems also have multiservers (which model multi-threaded software processes), multiple classes of service, and what we call deferred operations or "second phases", which are executed after sending the reply message to the requester. Three established MVA approximations for multiclass multiservers are extended to include deferred service, and evaluated within the layered queueing context. Errors ranged from 1% up to about 15%. These servers were then used to model the network file system, as implemented on Linux, to show that the method scales up and gives good accuracy on typical systems, with computation times of a few seconds to a few minutes. This is hundreds of times faster than simulation.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it