Cloud Service Negotiation: Concession vs. Tradeoff Approaches
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
For Cloud services, their non-functional properties like availability, reliability and security are important differentiators. However, service consumers and service providers may conflict over non-functional properties. In fact, the conflicts can be resolved via automated negotiation, which is considered as the most flexible approach to procure products and services. In this paper, we propose tradeoff approaches for Cloud service negotiation, and compare them with concession ones. As opposed to concession ones, tradeoff approaches do not reduce one's utility, but still can create a proposal attractive to its opponent. Indeed, simulation results show that tradeoff approaches outperform concession ones in terms of both individual utility and social benefit. However, simulation results also demonstrate that tradeoff approaches under perform concession ones in terms of success rate.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.004 |
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 it