Optimal allocation approach of virtual servers in cloud computing
Bibliographic record
Abstract
In this paper an optimal allocation approach of virtual servers to appropriate data centers is proposed for a network made up of collections of data centers and user groups. The virtual server allocation can be formalized as a linear programming (LP) problem which consists in minimizing the round-trip-times (latencies) between data centers and user groups. Our proposed allocation approach demonstrated good capabilities when evaluated under round-trip-time and group requirements (workload) changes. In compliance with the numerical results that are obtained using our proposed allocation approach, we observed that as long as the data center saturation is not achieved, increases in group requirements results in increases in the data center utilization percentage while the optimum doesn't change. However, when approaching the data center saturation our proposed approach ineluctably switches to another optimum resulting in a new reallocation of virtual servers. In contrast, by now varying round-trip-times between user groups and data centers neither data center utilization nor virtual server migration vary as long as the optimum remains unchanged. Nevertheless, beyond certain values of round-triptimes this optimum may obviously change inducing a virtual server re-allocation and variations in the data center utilization.
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.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".