Stratus ML: A Layered Cloud Modeling Framework
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
The main quest for cloud stakeholders is to find an optimal deployment architecture for cloud applications that maximizes availability, minimizes cost, and addresses portability and scalability. Unfortunately, the lack of a unified definition and adequate modeling language and methodologies that address the cloud domain specific characteristics makes architecting efficient cloud applications a daunting task. This paper introduces Stratus ML: a technology agnostic integrated modeling framework for cloud applications. Stratus ML provides an intuitive user interface that allows the cloud stakeholders (i.e., providers, developers, administrators, and financial decision makers) to define their application services, configure them, specify the applications' behaviour at runtime through a set of adaptation rules, and estimate cost under diverse cloud platforms and configurations. Moreover, through a set of model transformation templates, Stratus ML maintains consistency between the various artifacts of cloud applications. This paper presents Stratus ML and illustrates its usefulness and practical applicability from different stakeholder perspectives. A demo video, usage scenario and other relevant information can be found at the Stratus ML webpage.
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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 | 0.000 |
| 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