Paradigm-based adaptive provisioning in virtualized data centers
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
Virtualized data centers host multiple applications with distinct objectives in a shared infrastructure. Accommodating several dynamic applications in virtual data centers is a challenging task for cloud providers. Current provisioning solutions focus on a limited set of objectives that may not be suited for the increasing number of applications deployed in data centers everyday. In this paper we propose an adaptive provisioning architecture for virtualized data centers based on allocation paradigms. A paradigm translates high-level application goals to objectives, allocator instances, and actions that actually provision customized virtual infrastructures to applications. A paradigm policy language is defined to express the relationship between paradigms, objectives, and actions. A performance evaluation of the proposed approach considers four main aspects: acceptance ratio, provisioning cost, and CPU and link utilization. Simulation results show that our proposal is able to select the most appropriate set of allocation actions based on the particularities of the applications.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.002 |
| 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