IMPROMPTU: A Reactive and Distributed Resource Consolidation Manager for Clouds
Why this work is in the frame
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Bibliographic record
Abstract
In this paper, we present IMPROMPTU, a distributed resource consolidation manager for larger-scale commodity computing clouds. The main contribution of this work is two-fold. First, IMPROMPTU fully distributes the responsibility of resource consolidation management among autonomous node agents that have a one-to-one mapping with the physical machines in the cloud. Second, autonomous node agents manage virtual to physical machine resource consolidation using multiple criteria decision analysis (MCDA) through PROMETHEE II method. MCDA has been previously used within the context of computing systems, particularly in fields such as multi-agent systems, data mining, and wireless communications. However, to the best of our knowledge, IMPROMPTU represents the first fully distributed MCDA approach applied to the problem of autonomous resource consolidation management for commodity computing clouds. Moreover, IMPROMPTU improves on our previous studies by introducing key extensions to enhance the granularity of the MCDA model. Simulation results show that the proposed solution provides a strong alternative to prior resource consolidation management approaches for the key industry problem of mitigating SLA violations. This establishes solid groundwork for further applications and extensions of MCDA to this important problem domain.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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