Online Allocation of Cloud Resources Based on Security Satisfaction
Bibliographic record
Abstract
Businesses are becoming increasingly interested in exploiting the Cloud Computing technology. However, Cloud insecurity is still among the main factors that are blocking the full migration towards this paradigm. Increasing security investments will speed up the Cloud adoption process and improve the trustworthiness of the Cloud Service Providers (CSP). Moreover, the integration of the security element into the process of resource allocation will help increase the protection of the deployed services. However, this integration requires suitable modeling of customers' security requirements and CSPs' security offerings. To this end, we propose in this paper a broker-based model for the allocation of resources in the Cloud based on service security satisfaction. The resource allocation problem is modeled as a linear optimization problem and solved using an Evolutionary Computation approach, namely, the Genetic Algorithm (GA). The objective is to maximize the global security satisfaction of users' services by placing them on the data centers that adhere the most to their security requirements. Results show that the GA achieves an acceptable approximation of the optimal solution and is computationally efficient, which makes it suitable to function in online mode and cope with the scalability of the Cloud environment.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".