Rationalizing the Cloud Computing Concept: An Analogy with the Car
Bibliographic record
Abstract
Cloud Computing has quickly become a popular buzzword in the IT industry. It is hard to understand what cloud computing technologies are and if we are using the real thing. Many standard-developing organizations such as the National Institute of Standards and Technology (NIST) and the International Standards Organization (ISO) are working to provide a standard definition of Cloud Computing. Unfortunately, they are already facing a big challenge due to the presence of multiple definitions and the lack of understanding of this emerging technology. The main objective of this paper is to try to characterize Cloud Computing components and concepts using a familiar object, the car. Using this analogy, we identify concepts that should not be part of the international Cloud Computing definition on our quest for an improved definition. This work is part of the Canadian members' ISO/JTC1/SC38 work group effort aiming at an international consensus.
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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 | 0.009 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.006 | 0.044 |
| Scholarly communication | 0.013 | 0.020 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.004 | 0.007 |
| 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".