Proposal and Evaluation of a Dynamic Resource Allocation Method Based on the Load of VMs on IaaS
Bibliographic record
Abstract
Recently, Cloud computing has emerged as a new computing paradigm on the Internet. Cloud computing facilitates flexible and efficient computer resource management via virtualization technologies at anytime and from anywhere, so that users can add and/or delete IT resources. Users can set up and boot the required resources and they have to pay only for the required resources. However, they have to spend a considerable amount of time and money to design, set up, boot, and monitor their resources. Thus, in the future, providing a mechanism for efficient resource assignment and management will be an important objective of cloud computing. In this paper, we propose a dynamic resource allocation method based on the load of VMs on IaaS, abbreviated as DAIaS. This method enables users to dynamically add and/or delete one or more instances on the basis of the load and the conditions specified by the user. We implement a prototype to evaluate the effectiveness and efficiency of DAIaS. Furthermore, we perform an experiment to extract the prototype on a real cloud service, namely, Amazon EC2.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".