A Cooperative Game for Online Cloud Federation Formation Based on Security Risk Assessment
Bibliographic record
Abstract
Cloud federations allow Cloud Service Providers (CSPs) to deliver more efficient service performance by interconnecting their Cloud environments and sharing their resources. However, the security of the federated Cloud service could be compromised if the resources are shared with relatively insecure and unreliable CSPs. In this paper, we propose a Cloud federation formation model that considers the security risk levels of CSPs. We start by quantifying the security risk of CSPs according to well defined evaluation criteria related to security risk avoidance and mitigation, then we model the Cloud federation formation process as a hedonic coalitional game with a preference relation that is based on the security risk levels and reputations of CSPs. We propose a federation formation algorithm that enables CSPs to cooperate while considering the security risk introduced to their infrastructures, and refrain from cooperating with undesirable CSPs. According to the stability-based solution concepts that we use to evaluate the game, the model shows that CSPs will be able to form acceptable federations on the fly to service incoming resource provisioning requests whenever required.
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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 | 0.000 | 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 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".