A Framework for Classification of Resource Consolidation Management Problems
Bibliographic record
Abstract
Much effort in the current literature has been put towards methods and implementations to solve Resource Consolidation Management (RCM) problems in the cloud setting. A vast number of proposed solutions appears to be designed for different variants of the RCM problem. This makes the comparison of approaches challenging. We propose a new framework that facilitates mapping RCM solutions to their RCM problem definitions. Our framework allows a solution to be assigned to its RCM problem definition by means of answering a set of questions specific to RCM problems. Our framework can be used to (1) specify problem descriptions, (2) establish optimal solutions and providing theoretical benchmarks, (3) provide a platform allowing formal complexity analysis of RCM problems and (4) facilitate a healthy discussion about the essence of RCM and evaluations of different solutions. We show how our proposed framework can be applied in form of case studies depicting four approaches from the literature.
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How this classification was reachedexpand
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".