Bibliographic record
Abstract
We review the principles of computer performance modelling with queues, and validate the models using the empirical data. We focus on modelling a special configuration of two computers working in tandem: a mirroring system. Distributed system under investigation has implemented as a series of Java applets communicating using the functions of java net.* package. The mirroring aspect of the system is modelled analytically with a difference queue. The difference queue is defined for a network of two queues in parallel, as the queue consisting of elements present in one queue and absent from the other queue. The queueing models under investigation are M/M/1, and M/D/1 queues. In our study we have observed that the service times were not completely random on a typical server and could not be approximated well by exponential distribution. Our results have shown that the service was almost (i.e. with a very small variance) deterministic. We have also found that the d-queue model approximates well the behaviour of the mirroring system under low load conditions, and also provides an upper bound on the delays under high load conditions.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".