CLOUDQUAL: A Quality Model for Cloud Services
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
Cloud computing is an important component of the backbone of the Internet of Things (IoT). Clouds will be required to support large numbers of interactions with varying quality requirements. Service quality will therefore be an important differentiator among cloud providers. In order to distinguish themselves from their competitors, cloud providers should offer superior services that meet customers' expectations. A quality model can be used to represent, measure, and compare the quality of the providers, such that a mutual understanding can be established among cloud stakeholders. In this paper, we take a service perspective and initiate a quality model named CLOUDQUAL for cloud services. It is a model with quality dimensions and metrics that targets general cloud services. CLOUDQUAL contains six quality dimensions, i.e., usability, availability, reliability, responsiveness, security, and elasticity, of which usability is subjective, whereas the others are objective. To demonstrate the effectiveness of CLOUDQUAL, we conduct empirical case studies on three storage clouds. Results show that CLOUDQUAL can evaluate their quality. To demonstrate its soundness, we validate CLOUDQUAL with standard criteria and show that it can differentiate service quality.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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