C2TOP: A Cloud Competition-based Truthful Online Pricing Mechanism in Secondary Markets
Bibliographic record
Abstract
Flexibility and the ability of cost management have made on-demand price scheduling as one of the most favourable choices of cloud service users. This mechanism is not appealing for cloud service providers at the same level because it cannot provide the required information to anticipate and provide future market demands. Instead, cloud service providers are more inclined to use Futures contracts. However, these methods do not provide the flexibility and cost management that on-demand methods provide for service users. Cloud secondary markets emerged to meet the needs of both sides of the market: 1) cloud service providers, 2)cloud service users, where brokers and reseller buyers act as intermediaries to provide the demanded services for service users. There is high competition among service providers to attract service users and among service users to find appropriate services, and also there are high fluctuations in environmental parameters, such as price and the volume of supply and demand. These are the reasons that designing an appropriate pricing algorithm and resource allocation mechanism is of great importance. In this paper, we propose a competition-based price scheduling mechanism that considers the inherent competition and fluctuation attributes of such environments. The provided experimental results show that our mechanism is superior to other methods, such as fixed and history-based pricing mechanisms, in terms of utility and resource allocation efficiency.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 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".