Towards Security-based Formation of Cloud Federations: A Game Theoretical Approach
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 service could be compromised if the resources are shared with relatively insecure CSPs, and violations of the Security Service Level Agreement (Security-SLA) might occur. In this paper, we propose a Cloud federation formation model that considers the security level of CSPs. We start by applying the Goal-Question-Metric (GQM) method to develop a set of parameters that quantitatively describes the Security-SLA in the Cloud, and use it to evaluate the security levels of the CSPs and formed federations with respect to a defined Security-SLA baseline, while taking into account CSPs' customers' security satisfaction. Then, we model the Cloud federation formation process as a hedonic coalitional game with a preference relation that is based on the security level and reputation of CSPs. We propose a federation formation algorithm that enables CSPs to join a federation while minimizing their loss in security, and refrain from forming relatively insecure federations. Experimental results show that our model helps maintaining higher levels of security in the formed federations and reducing the rate and severity of Security-SLA violations.
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".