Financial Option Market Model for Federated Cloud Environments
Bibliographic record
Abstract
Pay-per-use service by Cloud service providers has attracted customers in the recent past and is still evolving. Since the resources being dealt within Clouds are non-storable and the physical resources need to be replaced very often, pricing the service in a way that would return profit on the initial capital investments to the service providers has been a major issue. Moreover, to maintain Quality of Service (QoS) to customers who reserve the resources in advance and may or may not be using the resources at a future date makes the resources wasted, if not allocated to other on-demand users. Therefore, a need for a mechanism to guarantee the resources to reserved users whenever they need them, while keeping the resources busy all the time is in very high demand. The concept of federation of Cloud service providers has been proposed in the past wherein resources are traded between the providers whenever need arises. We propose a financial option based Cloud resources pricing model to address the above situation. This model allows a provider to hedge the critical and risky situation of reserved users requesting the resources while all the resources have been allocated to other users, by trading (buying or outsourcing) resources from other service providers in the Cloud federation. We show that using financial option based contracts between Cloud providers in a Cloud federation, providers are able to enhance profit and acquire the needed resources at any given time. It would also help creating a trust and goodwill from the clients on the Cloud service providers by less number of QoS violation.
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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 | 0.002 | 0.005 |
| 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 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.015 | 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".