Autonomous Resource Consolidation Management in Clouds Using IMPROMPTU Extensions
Bibliographic record
Abstract
This paper focuses on the problem of resource consolidation management within cloud computing environments and extends our previous IMPROMPTU model which demonstrated the viability of distributed Multiple Criteria Decision Analysis (MCDA) to provide a resource consolidation management that simultaneously achieves lower numbers of reconfiguration events and fewer service level agreement (SLA) violations as compared to other approaches. A core limitation of our previous work was that it only assessed the PROMETHEE II outranking-based MCDA method, leaving open the question of whether better outranking schemes exist and, more generally, what denotes the properties of good outranking approaches for this problem domain. This work addresses these deficiencies through extending the IMPROMPTU model to directly compare PROMETHEE II with ELECRE III and PAMSSEM II, two other well-known outranking-based MCDA schemes. An in-depth analysis of the generated simulation results are then used to highlight the core trade-offs between each of these MCDA approaches.
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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 | 0.001 | 0.001 |
| 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".