Associating Performance Measures with Perceived End User Performance: ISO 25023 compliant Low Level Derived Measures
Bibliographic record
Abstract
This paper applies a measurement procedure to predict the degraded state of a private cloud application using only available data center log low level derived measures (LLDM). Our intent is to improve the discussion of service level agreements of a widely used private cloud computing application (i.e. 80,000 users on 600 servers world-wide). In organizations, cloud application performance measuring is often based on subjective and qualitative measures with very few researches to address the large-scale private cloud perspective. Furthermore, measurement recommendations from ISO proposals (i.e. ISO 250xx series, ISO/IEC 15939 and more recently the ISO/SC38-SLA framework) are poorly adopted by the industry, mainly due to the absence of proof of concept and the high degree of complexity associated with implementing the measurement concepts described in these international standards. To try to demonstrate these concepts, the ISO 25010 performance efficiency characteristics are used with a number of LLDMs to model the state of a large private cloud computing application using indicators such as: normal, abnormal, adequate or degraded. This application still cannot be generalized due to its nature as research in progress.
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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 | 0.007 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".