Service Assignment in Federated Cloud Environments Based on Multi-objective Optimization of Security
Bibliographic record
Abstract
Cloud federation allows interconnected Cloud Computing environments of different Cloud Service Providers (CSPs) to share their resources and deliver more efficient service performance. However, each CSP provides a different level of security in terms of cost and performance. Instead of consuming the whole set of Cloud services that are required to deploy an application through a single CSP, consumers could benefit from the Cloud federation and flexibly assign the services to multiple CSPs in order to satisfy all their services' security requirements. In this paper, we model the service assignment problem in federated Cloud environments as a Multi-objective optimization problem based on security. The model allows consumers to consider a trade-off between three security factors: cost, performance, and risk, when assigning their services to CSPs. The cost and performance of the delivered security services are evaluated using a set of quantitative metrics which we propose. We then solve the problem using the preemptive optimization method which permits to take into consideration the customer's priorities. Simulations showed that the model helps in reducing the rate of security and performance 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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".