A performance evaluation framework for Web 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
ABSTRACT Performance engineering for Web applications must take into account both the development and runtime information about the target system and its environment. At development time, the architects have to choose from many architecture styles and consider all performance requirements across a multitude of workloads. At runtime, an Autonomic Manager has to compensate for the changing operating and environment conditions not accounted for at the design time and make decisions about changes in the architecture so the performance requirements are met. This paper proposes a formal framework called Software Performance for Autonomic Computing for making decisions with regard to a possible set of candidate architectures: usage scenarios are criteria according to which architectures are evaluated; actual performance metrics, such as response time or throughput, are obtained by solving performance models and then matched against the performance requirements; performance requirements are defined by modeling user satisfaction with a utility function. Criteria can be weighted to reflect their importance. The framework can be used both at design and run time. Copyright © 2012 John Wiley & Sons, Ltd.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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