A decision framework for cloud migration: A hybrid approach
Why this work is in the frame
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Bibliographic record
Abstract
Abstract Cloud computing is utilised for information technology outsourcing of either industries or organisations. There are several inhibitors and motivations related factors to determine whether one can embrace the cloud services or not. Therefore, this paper presents a holistic and flexible cloud decision framework by taking a wide spectrum weighted factors related to the acceptance or denial of the cloud services. To have sustainable decision and obviating the shortcomings of the existing approaches on the cloud service adoption, a deep understanding of organisation's business process requirements and cost implications is required. To utilise the proposed model, the functional and non‐functional requirements associated to the business process of an adopter organisation must be specified. To reach a concrete decision, a hybrid approach is applied by incorporating the analytic hierarchy process and Delphi methods to prevent subjective outcomes and to have diverse experiences at the same time. To support the decision model, some economic theories and Moore law are used. To verify the proposed model, a Telecommunication Company is considered as a case study for its 6‐year plan of investment. The simulation results of conducted scenarios for the mentioned mid‐scale case study prove that it is logical to establish on‐premises a private datacenter and utilising the hybrid deployment once it encounters abrupt burst of resource demand. Altogether, the proposed holistic model can be customised for different users with different scales.
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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 | 0.000 | 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 | 0.000 | 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