An analysis of first fit heuristics for the virtual machine relocation problem
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
In recent years, data centres have come to achieve higher utilization of their infrastructure through the use of virtualization and server consolidation (running multiple application servers simultaneously in one physical server). One problem that arises in these consolidated environments is how to deal with stress situations, that is, when the combined demand of the hosted virtual machines (VMs) exceeds the resource capacity of the host. The VM Relocation problem consists of determining which VMs to migrate and to which hosts to migrate them, so as to relieve the stress situations. In this paper, we propose that the order in which VMs and hosts are considered for migration results in better outcomes, depending on the situation and the data centre's business goals. We evaluate and compare a set of First Fit-based relocation policies, which consider VMs and hosts in different order. We present simulation results showing that the policies succeed to different extents depending on the scenario and the metrics observed.
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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 | 0.001 |
| 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