Proposal and Evaluation of a Dynamic Resource Allocation Method Based on the Load of VMs on IaaS
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
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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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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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