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 machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.013 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.003 | 0.008 |
| Scholarly communication | 0.010 | 0.012 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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 source (direct Gemma or distilled Codex), 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".