Cloud Security Up for Auction: a DSIC Online Mechanism for Secure IaaS Resource Allocation
Bibliographic record
Abstract
The lack of security is still one of the main factors that are blocking the full migration towards the Cloud Computing paradigm. More businesses would be attracted by the adoption of the Cloud model if the Cloud Infrastructure Providers (CIP) start increasing their investment in security solutions and demonstrating more explicitly the potency of their infrastructures in protecting customers' data and services. However, implementing security solutions is usually costly, and does not necessarily generate higher revenues. One way to reduce the cost of security investments would be to rent the Cloud secure resources to customers in a competitive fashion. The CIP could place her added value of security up for auction, and customers would place their bids along with their resource provisioning requests. In this paper, we propose an online mechanism that performs the allocation of the CIP's resources to customers in a security-oriented auction-based fashion. The mechanism is Dominant-Strategy Incentive-Compatible (DSIC), i.e., it guaranties the truthfulness of the bidders. The mechanism aims at allocating the resources to customers who valuate their security the most, and shows an acceptable performance compared to the famous offline Vickrey-Clarke-Groves (VCG) truthful mechanism.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".