Cloud Service Negotiation: A Research Roadmap
Bibliographic record
Abstract
Cloud services are Internet-based XaaS (X as a Service) services, where X can be hardware, software or applications. As Cloud consumers value QoS (Quality of Service), Cloud providers should make certain service level commitments in order to achieve business success. This paper argues for Cloud service negotiation. It outlines a research roadmap, reviews the state of the art, and reports our work on Cloud service negotiation. Three research problems that we formulate are QoS measurement, QoS negotiation, and QoS enforcement. To address QoS measurement, we pioneer a quality model named CLOUDQUAL for Cloud services. To address QoS negotiation, we propose a tradeoff negotiation approach for Cloud services, which can achieve a higher utility. We also give some ideas to solve QoS enforcement, and balance utility and success rate for QoS negotiation.
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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 | 0.023 | 0.020 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.006 | 0.012 |
| Science and technology studies | 0.003 | 0.008 |
| Scholarly communication | 0.017 | 0.043 |
| Open science | 0.006 | 0.006 |
| Research integrity | 0.015 | 0.011 |
| Insufficient payload (model declined to judge) | 0.011 | 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 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".