QuARAM Recommender: Case-Based Reasoning for IaaS Service Selection
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
Cloud computing enables elastic resource provisioning on demand and removes the boundaries of resources' physical locations. The number of cloud-based services is on the rise due to the growing interest from both providers and consumers. These services are characterized by a large number of features or properties, which makes the automatic service selection and deployment challenging. This paper proposes QuRAM Recommender, a cloud infrastructure service recommender framework based on case-based reasoning (CBR) that supports effective service selection. QuRAM Recommender supports decision making that accommodates the customer's preferences and feedback. We show the feasibility of our approach through a prototype implementation that elaborates on the main features of our system. The experimental results suggest that case-based reasoning is a viable option for recommending cloud services that best fit the customer's requirements.
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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 | 0.001 | 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 | 0.000 | 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