Multi-Unit Market-based Mechanism Design in Cloud Secondary Markets
Bibliographic record
Abstract
The on-demand pricing mechanism, which is one of the most common pricing methods for cloud services, implements the pay-as-you-go model for cloud service users. Moreover, it provides the possibility of cost management and the flexibility of the service time for their service users. Since the on-demand mechanism does not provide all the providers' required information, it is not of interest to them. Thus, the service providers are more inclined to use futures contracts so they can accurately estimate the future needs of their users and plan for the requirements. To add the users' required flexibility to futures contracts, a new concept is introduced to cloud ecosystems, called the secondary market. In this secondary market, brokers and reseller buyers act as mediators to provide the required VMs for the service users. This paper provides a mechanism design that includes a market-based pricing model and a resource allocation mechanism in such environments. The proposed mechanism is based on dynamic double auction models and the suggested market price is computed based on the critical point that is obtained from the presence of the agents. Our experimental results prove that the proposed mechanism outperforms the other algorithms in terms of the overall utility, and the 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 | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".