A Resource Allocation Model Based on Trust Evaluation in Multi-Cloud Environments
Bibliographic record
Abstract
Allocating and managing resources while considering the quality of service is considered a fundamental and complex research problem in a cloud environment. An optimal resource allocation optimizes several parameters such as optimizing cost and resource utilization or maximizing any quality parameters. However, to ensure better customer service, Cloud Service Providers (CSPs) should consider most of the quality attributes while allocating resources to the cloud infrastructures. Existing research does not evaluate trust as a quantitative attribute, thus a trade-off between trust and performance in resource allocation is also absent in the research area. We propose a model to consider both trust and delay in this paper. The trust of a CSP is quantitatively estimated through some attributes and metrics. Availability, reliability, data integrity, and efficiency are considered to estimate the trust. The objective is to maximize the trust of the allocation while minimizing the communication delay. The proposed joint optimization model combines the previous credentials of the CSPs and the present resource constraints. To solve the problem heuristically, a genetic algorithm is applied. The model uses a number of parameters that provide the flexibility to adapt several service requirements. The effectiveness and applicability of the proposed approach are demonstrated through experiments. The results ensure that the effectiveness in the estimation of trust evaluation for different CSPs with the proposed attributes. Moreover, integrating trust in the resource allocation model allocates appropriate resources while enhancing the trust and reducing the communication delay in the overall allocation.
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How this classification was reachedexpand
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 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".