StratusPM: An Analytical Performance Model for Cloud 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
Several planning and performance analysis tools and techniques are available to execute sophisticated "what-if" analysis, in order to dynamically reconfigure cloud applications, modify them, and update their adaptation policies. However, in order to utilize these tools and techniques, appropriate analytical models for such cloud software systems must first be built. Constructing such models is difficult, as it requires the knowledge of: (i) the architecture of the system (i.e., deployment model), (ii) the specifications of the platform, (iii) the behavior of the application at runtime, and (iv) the formalism and notations of the target analytical model. This is further complicated in the cloud due to: (i) the fluid nature of the cloud application models and platforms, (ii) the large number of platform providers, and (iii) the successive upgrade to the underlying platforms. In our previous research work, we developed an architectural framework and a modeling language for the cloud configuration space called StratusML. This paper aims at extending StratusML to support generating analytical performance models for cloud applications by reusing the information used to configure the platform for the deployment and the operation. We show through an example, how to use StratusPM to model cloud performance and how the new extension can help reducing the efforts needed to specify analytical performance models for cloud applications.
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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 | 0.000 |
| 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