Mean value analysis for computer systems with variabilities in workload
Bibliographic record
Abstract
When evaluating the performance of computer systems, often uncertainties or variabilities in service demands may be observed. Applying well known mean valve analysis (MVA) for single- or multiclass queueing network models of such systems is inappropriate and ineffective, because these models fail to represent variations within a class. This paper proposes to use histograms for characterizing model parameters that are associated with uncertainty or variability and presents an adaptation of the single class MVA algorithm, which traditionally accepts single (mean) values for service demands, so that one or more input parameters can be specified as a histogram. The adapted algorithm generates a histogram output for the performance measures, thus providing a more detailed information (e.g. percentile values) than the mean valves obtained from conventional MVA. The proposed technique is demonstrated on selected examples in different problem domains. It is shown, that the computational complexity is reasonable given that the number of parameters specified as histograms is not too high. Although the algorithm produces accurate results in many situations inaccuracies have been observed for certain systems. A technique called interval splitting that can be used for controlling such inaccuracies is described.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".