De-Centralized Reputation-Based Trust Model to Discriminate between Cloud Providers Capable of Processing Big Data
Bibliographic record
Abstract
Trust and reputation systems represent a significant trend in decision support including selection of best match cloud providers to process Big Data. Reputation is often considered as a collective measure of trustworthiness based on the referrals or ratings from members in a community. Reputation systems have been applied in various applications such as online service provision. However, reputation models do not reflect user's quality of service (QoS) preferences and thus they might not be satisfied with the recommendations from others. In this paper, we propose a de-centralized reputation-based trust model that incorporates the user QoS preferences to select the best match Cloud Service Provider to process Big Data. Our trust model relies on three multi-attribute decision-making (MADM) methods including Simple Additive Weighting (SAW), Weighted Product Method (WPM), and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS). We conducted several experiments using simulated cloud environment to validate our trust model and assess the three MADM methods. The results show that the proposed model is pliable to users' requirements and efficiently evaluate trust of cloud providers.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".